{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "c979f719-a7bd-4392-86f9-f699220b2c93",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
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       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>STATEFP20</th>\n",
       "      <th>COUNTYFP20</th>\n",
       "      <th>TRACTCE20</th>\n",
       "      <th>GEOID20</th>\n",
       "      <th>NAME20</th>\n",
       "      <th>NAMELSAD20</th>\n",
       "      <th>MTFCC20</th>\n",
       "      <th>FUNCSTAT20</th>\n",
       "      <th>ALAND20</th>\n",
       "      <th>AWATER20</th>\n",
       "      <th>...</th>\n",
       "      <th>P0050002</th>\n",
       "      <th>P0050003</th>\n",
       "      <th>P0050004</th>\n",
       "      <th>P0050005</th>\n",
       "      <th>P0050006</th>\n",
       "      <th>P0050007</th>\n",
       "      <th>P0050008</th>\n",
       "      <th>P0050009</th>\n",
       "      <th>P0050010</th>\n",
       "      <th>geometry</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>18</td>\n",
       "      <td>089</td>\n",
       "      <td>030700</td>\n",
       "      <td>18089030700</td>\n",
       "      <td>307</td>\n",
       "      <td>Census Tract</td>\n",
       "      <td>G5020</td>\n",
       "      <td>S</td>\n",
       "      <td>4978681</td>\n",
       "      <td>140267</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>POLYGON ((-87.46582 41.62928, -87.46554 41.629...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>18</td>\n",
       "      <td>089</td>\n",
       "      <td>030800</td>\n",
       "      <td>18089030800</td>\n",
       "      <td>308</td>\n",
       "      <td>Census Tract</td>\n",
       "      <td>G5020</td>\n",
       "      <td>S</td>\n",
       "      <td>941893</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>POLYGON ((-87.45698 41.64567, -87.45679 41.645...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>18</td>\n",
       "      <td>089</td>\n",
       "      <td>030900</td>\n",
       "      <td>18089030900</td>\n",
       "      <td>309</td>\n",
       "      <td>Census Tract</td>\n",
       "      <td>G5020</td>\n",
       "      <td>S</td>\n",
       "      <td>1945376</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>4</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>4</td>\n",
       "      <td>POLYGON ((-87.45444 41.63359, -87.45420 41.633...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>18</td>\n",
       "      <td>003</td>\n",
       "      <td>003100</td>\n",
       "      <td>18003003100</td>\n",
       "      <td>31</td>\n",
       "      <td>Census Tract</td>\n",
       "      <td>G5020</td>\n",
       "      <td>S</td>\n",
       "      <td>1647995</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>POLYGON ((-85.13736 41.05009, -85.13702 41.050...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>18</td>\n",
       "      <td>089</td>\n",
       "      <td>042401</td>\n",
       "      <td>18089042401</td>\n",
       "      <td>424.01</td>\n",
       "      <td>Census Tract</td>\n",
       "      <td>G5020</td>\n",
       "      <td>S</td>\n",
       "      <td>7312127</td>\n",
       "      <td>54423</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>POLYGON ((-87.39394 41.52262, -87.39353 41.522...</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 345 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "  STATEFP20 COUNTYFP20 TRACTCE20      GEOID20  NAME20    NAMELSAD20 MTFCC20  \\\n",
       "0        18        089    030700  18089030700     307  Census Tract   G5020   \n",
       "1        18        089    030800  18089030800     308  Census Tract   G5020   \n",
       "2        18        089    030900  18089030900     309  Census Tract   G5020   \n",
       "3        18        003    003100  18003003100      31  Census Tract   G5020   \n",
       "4        18        089    042401  18089042401  424.01  Census Tract   G5020   \n",
       "\n",
       "  FUNCSTAT20  ALAND20  AWATER20  ... P0050002 P0050003 P0050004 P0050005  \\\n",
       "0          S  4978681    140267  ...        0        0        0        0   \n",
       "1          S   941893         0  ...        0        0        0        0   \n",
       "2          S  1945376         0  ...        0        0        0        0   \n",
       "3          S  1647995         0  ...        0        0        0        0   \n",
       "4          S  7312127     54423  ...        0        0        0        0   \n",
       "\n",
       "  P0050006 P0050007 P0050008 P0050009 P0050010  \\\n",
       "0        0        0        0        0        0   \n",
       "1        0        0        0        0        0   \n",
       "2        0        4        0        0        4   \n",
       "3        0        0        0        0        0   \n",
       "4        0        0        0        0        0   \n",
       "\n",
       "                                            geometry  \n",
       "0  POLYGON ((-87.46582 41.62928, -87.46554 41.629...  \n",
       "1  POLYGON ((-87.45698 41.64567, -87.45679 41.645...  \n",
       "2  POLYGON ((-87.45444 41.63359, -87.45420 41.633...  \n",
       "3  POLYGON ((-85.13736 41.05009, -85.13702 41.050...  \n",
       "4  POLYGON ((-87.39394 41.52262, -87.39353 41.522...  \n",
       "\n",
       "[5 rows x 345 columns]"
      ]
     },
     "execution_count": 1,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# this is for working with census tracts and VTD precincts\n",
    "from shapely.geometry import Point, LineString, Polygon\n",
    "import shapely\n",
    "import geopandas as gpd\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "from numpy import random\n",
    "from scipy.stats import norm\n",
    "import math\n",
    "#tractGeomFile = gpd.read_file(\"state_map_files/fl_pl2020_t.shp\")  #Boo!  Only has geometries.  do not use\n",
    "tractPopFile = gpd.read_file(\"state_map_files/in_pl2020_t.dbf\") #for Texas, need only this file\n",
    "tractPopFile.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "a7a90b04-4b48-4ab8-9195-8d953907c3fb",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "there are 1696 popn tracts for IN\n"
     ]
    }
   ],
   "source": [
    "# EXTRACT TRACT GEOMETRIES AND POPULATIONS INTO LISTS, COMPUTE TRACT AREAS\n",
    "# If the population and geometry data are in one file, this should work.\n",
    "STATE = \"IN\"\n",
    "tractGeom = tractPopFile['geometry']  #for some states, replace with tractGeomFile\n",
    "tractPop = tractPopFile['P0010001']\n",
    "tractVAP = tractPopFile['P0030001']   #NEW 3/2/22 - USE VAP\n",
    "# tractPop2 = tractPopFile['P0020001']   #not needed; confirmed that this matches P00100001 exactly\n",
    "tractHisp = tractPopFile['P0040002']   #NEW 3/2/22 - USE VAP\n",
    "tractBlack = tractPopFile['P0030004']  #NEW 3/2/22 - USE VAP\n",
    "nTracts = len(tractPop)\n",
    "print(\"there are {0} popn tracts for {1}\".format(nTracts, STATE) )\n",
    "tractArea = [0.]*nTracts\n",
    "for t in range (0,nTracts) :\n",
    "    tractArea[t] = tractGeom[t].area\n",
    "isSkippedTract = [0] *nTracts  #this will house a temporary list of tracts for manipulation\n",
    "tractPop = tractPop.to_numpy()  #to avoid panda overwrite grousing\n",
    "tractBlack= tractBlack.to_numpy()\n",
    "tractHisp = tractHisp.to_numpy()\n",
    "tractVAP = tractVAP.to_numpy()\n",
    "stateVAP = np.sum(tractVAP)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "c90b3908-e2e3-46b8-98e5-ae285eca1ee3",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "this is Hispanic population by total population IN voter-age population\n",
      "state pop= 6785528 VAP pct Hispanic is  0.06758279459975476\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#What is our correlation of Hispanic pop to total pop?\n",
    "print(\"this is Hispanic population by total population \"+STATE,\"voter-age population\")\n",
    "print(\"state pop=\",np.sum(tractPop), \"VAP pct Hispanic is \",np.sum(tractHisp)/np.sum(tractVAP) )\n",
    "fig, ax = plt.subplots()\n",
    "ax.set(xlabel=\"tract Voter Age Population\", ylabel=\"tract Hispanic pop\")\n",
    "x = [0,10000]\n",
    "y = [0,10000]\n",
    "plt.plot(tractVAP, tractHisp, marker='.',linestyle=\"none\")\n",
    "plt.plot(x,y,linestyle = 'dashed')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "237df16d-af87-47ba-acb1-2ef5702c1406",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "this is voter-age Black population by total VAP IN\n",
      "total state pop= 6785528 , VAP pct Black is  0.09010185497418527\n"
     ]
    },
    {
     "data": {
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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#What is our correlation of Black pop to total pop?\n",
    "print(\"this is voter-age Black population by total VAP \"+STATE)\n",
    "print(\"total state pop=\",np.sum(tractPop), \", VAP pct Black is \",np.sum(tractBlack)/np.sum(tractVAP) )\n",
    "fig, ax = plt.subplots()\n",
    "ax.set(xlabel=\"tract VAP\", ylabel=\"tract Black VAP\")\n",
    "x = [0,10000]\n",
    "y = [0,10000]\n",
    "plt.plot(tractVAP, tractBlack, marker='.',linestyle=\"none\")\n",
    "plt.plot(x,y,linestyle = 'dashed')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "10b208af-8eb0-45fa-9dc0-8873eb797200",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "this is a histogram of Census tract population for IN\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#for curiosity, let's plot the distribution of populations among tracts\n",
    "n_bins=50\n",
    "print(\"this is a histogram of Census tract population for \"+STATE)        \n",
    "fig, ax = plt.subplots(tight_layout=True)\n",
    "# We can set the number of bins with the *bins* keyword argument.\n",
    "ax.hist(tractPop, bins=n_bins)\n",
    "plt.show() "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "4aac60d6-4cce-469b-b075-ad743acda38c",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "this is population by Census tract for IN\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#What is our distribution of tract populations by area?\n",
    "print(\"this is population by Census tract for \"+STATE)  \n",
    "plt.plot(tractPop, tractArea, marker='.',linestyle=\"none\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "f159bf55-a25e-45c0-b7ea-4c21fbce9bb0",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'shapely.geometry.polygon.Polygon'> <class 'shapely.geometry.polygon.Polygon'>\n"
     ]
    }
   ],
   "source": [
    "print( type(tractGeom[0]),type(tractGeom[1]) )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "5e8f8f92-a336-43e5-8ac5-20c657cb5857",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "looking for nonPolygons in census tract data\n"
     ]
    }
   ],
   "source": [
    "print(\"looking for nonPolygons in census tract data\")\n",
    "notPoly = [0]*nTracts\n",
    "for m in range(nTracts):\n",
    "    if type(tractGeom[m]) != type(tractGeom[1]):  #assuming tract 1 is a single polygon\n",
    "        notPoly[m] = 1\n",
    "        print(m,tractPop[m], tractArea[m], \"nonPolygon tract no, pop, area\")\n",
    "        x = tractGeom[m].centroid.x\n",
    "        y = tractGeom[m].centroid.y\n",
    "        if y < 999 :  #let's zoom in on these\n",
    "            for geom in tractGeom[m].geoms :\n",
    "                xg,yg = geom.exterior.xy\n",
    "                print(geom.area)\n",
    "                plt.plot(xg,yg)\n",
    "            plt.text(x+0.0,y+0.01,m,fontsize=9)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "d34b5fe5-6f02-413a-8c7b-c6662fa756ad",
   "metadata": {},
   "outputs": [],
   "source": [
    "#convex-hull these right away  (decision for each state - n/a for IN)\n",
    "for m in range(nTracts):\n",
    "    if notPoly[m] == 1:\n",
    "        for geom in tractGeom[m].geoms :\n",
    "            xg,yg = geom.exterior.xy\n",
    "            plt.plot(xg,yg)\n",
    "        tractGeom[m] = tractGeom[m].convex_hull\n",
    "        x,y = tractGeom[m].exterior.xy\n",
    "        plt.plot(x,y)\n",
    "        notPoly[m] = 0\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "a05c0ec2-d208-45b8-8ff6-c1f1f583ad5d",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[538, 631, 908, 909, 912, 940, 1001, 1160, 1551]\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# For potential later issues, identify and flag lakeshore low-population tracts.\n",
    "lakeTracts = [-99999]\n",
    "minTractPop = 5\n",
    "for m in range(nTracts):\n",
    "    if tractPop[m] < minTractPop:\n",
    "        x = tractGeom[m].centroid.x\n",
    "        y = tractGeom[m].centroid.y\n",
    "        x2,y2 = tractGeom[m].exterior.xy  #turn these two on to show state map\n",
    "        plt.plot(x2,y2,c=\"purple\")\n",
    "        if 0 == 0 :  # y <38.8:  #only eliminate lake tracts south of Chesapeake Bay Bridge from map\n",
    "            plt.text(x, y,m, fontsize=9)\n",
    "            # isSkippedTract[m] = 1   #not yet\n",
    "            if lakeTracts == [-99999]:\n",
    "                lakeTracts = [m]\n",
    "            else:\n",
    "                lakeTracts.append(m)\n",
    "\n",
    "print(lakeTracts)  # not yet assigned as skipped tracts; let's view first\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "da1f99a3-04d0-427b-95e3-d9a57836959d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "skippedTracts = [631, 908, 909, 912, 940, 1001, 1160, 1551]  #tract 538 is an interior\n",
    "for t in skippedTracts:\n",
    "    x,y = tractGeom[t].exterior.xy\n",
    "    plt.plot(x,y)\n",
    "    isSkippedTract[t] = 1\n",
    "                \n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "b0e9e246-a558-475c-8181-075a9421034e",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1696 8\n"
     ]
    }
   ],
   "source": [
    "print(len(isSkippedTract),np.sum(isSkippedTract))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "d63968a2-afc1-4913-863c-4c415fb9c0b8",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "looking for tracts with zero area\n"
     ]
    }
   ],
   "source": [
    "# For potential later issues, identify and visualize zero-Area tracts.  See Ohio code for more code\n",
    "isSkippedTract = [0]*nTracts\n",
    "print(\"looking for tracts with zero area\")\n",
    "for m in range(nTracts):\n",
    "    if(tractArea[m] == 0):       \n",
    "        x = tractGeom[m].centroid.x\n",
    "        y = tractGeom[m].centroid.y\n",
    "        print( m,tractPop[m],\"(\",x,\",\",y,\")\" )\n",
    "        x2,y2 = tractGeom[m].exterior.xy\n",
    "        plt.plot(x2,y2,c=\"purple\")\n",
    "        #print(tractGeom[m])\n",
    "        plt.scatter(x, y)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "72abde7f-085d-4c0a-abe3-29e8d6203b40",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>STATEFP20</th>\n",
       "      <th>COUNTYFP20</th>\n",
       "      <th>VTDST20</th>\n",
       "      <th>GEOID20</th>\n",
       "      <th>NAME20</th>\n",
       "      <th>G20PRERTRU</th>\n",
       "      <th>G20PREDBID</th>\n",
       "      <th>G20PRELJOR</th>\n",
       "      <th>G20GOVRHOL</th>\n",
       "      <th>G20GOVDMYE</th>\n",
       "      <th>G20GOVLRAI</th>\n",
       "      <th>G20ATGRROK</th>\n",
       "      <th>G20ATGDWEI</th>\n",
       "      <th>geometry</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>18</td>\n",
       "      <td>123</td>\n",
       "      <td>000200</td>\n",
       "      <td>18123000200</td>\n",
       "      <td>TROY TOWNSHIP 01</td>\n",
       "      <td>538</td>\n",
       "      <td>367</td>\n",
       "      <td>12</td>\n",
       "      <td>580</td>\n",
       "      <td>277</td>\n",
       "      <td>53</td>\n",
       "      <td>487</td>\n",
       "      <td>402</td>\n",
       "      <td>POLYGON ((-86.80477 37.99381, -86.80435 37.993...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>18</td>\n",
       "      <td>123</td>\n",
       "      <td>000190</td>\n",
       "      <td>18123000190</td>\n",
       "      <td>TROY</td>\n",
       "      <td>91</td>\n",
       "      <td>59</td>\n",
       "      <td>4</td>\n",
       "      <td>90</td>\n",
       "      <td>49</td>\n",
       "      <td>12</td>\n",
       "      <td>81</td>\n",
       "      <td>65</td>\n",
       "      <td>POLYGON ((-86.81450 37.99871, -86.81355 37.998...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>18</td>\n",
       "      <td>123</td>\n",
       "      <td>000140</td>\n",
       "      <td>18123000140</td>\n",
       "      <td>TELL CITY 05</td>\n",
       "      <td>263</td>\n",
       "      <td>203</td>\n",
       "      <td>10</td>\n",
       "      <td>275</td>\n",
       "      <td>171</td>\n",
       "      <td>27</td>\n",
       "      <td>232</td>\n",
       "      <td>228</td>\n",
       "      <td>POLYGON ((-86.77842 37.95835, -86.77832 37.958...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>18</td>\n",
       "      <td>123</td>\n",
       "      <td>000160</td>\n",
       "      <td>18123000160</td>\n",
       "      <td>TELL CITY 07</td>\n",
       "      <td>207</td>\n",
       "      <td>185</td>\n",
       "      <td>9</td>\n",
       "      <td>219</td>\n",
       "      <td>158</td>\n",
       "      <td>21</td>\n",
       "      <td>182</td>\n",
       "      <td>207</td>\n",
       "      <td>POLYGON ((-86.78400 37.97424, -86.78236 37.974...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>18</td>\n",
       "      <td>123</td>\n",
       "      <td>000010</td>\n",
       "      <td>18123000010</td>\n",
       "      <td>ANDERSON</td>\n",
       "      <td>531</td>\n",
       "      <td>263</td>\n",
       "      <td>14</td>\n",
       "      <td>565</td>\n",
       "      <td>192</td>\n",
       "      <td>51</td>\n",
       "      <td>440</td>\n",
       "      <td>347</td>\n",
       "      <td>POLYGON ((-86.80715 38.11784, -86.80709 38.117...</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  STATEFP20 COUNTYFP20 VTDST20      GEOID20            NAME20  G20PRERTRU  \\\n",
       "0        18        123  000200  18123000200  TROY TOWNSHIP 01         538   \n",
       "1        18        123  000190  18123000190              TROY          91   \n",
       "2        18        123  000140  18123000140      TELL CITY 05         263   \n",
       "3        18        123  000160  18123000160      TELL CITY 07         207   \n",
       "4        18        123  000010  18123000010          ANDERSON         531   \n",
       "\n",
       "   G20PREDBID  G20PRELJOR  G20GOVRHOL  G20GOVDMYE  G20GOVLRAI  G20ATGRROK  \\\n",
       "0         367          12         580         277          53         487   \n",
       "1          59           4          90          49          12          81   \n",
       "2         203          10         275         171          27         232   \n",
       "3         185           9         219         158          21         182   \n",
       "4         263          14         565         192          51         440   \n",
       "\n",
       "   G20ATGDWEI                                           geometry  \n",
       "0         402  POLYGON ((-86.80477 37.99381, -86.80435 37.993...  \n",
       "1          65  POLYGON ((-86.81450 37.99871, -86.81355 37.998...  \n",
       "2         228  POLYGON ((-86.77842 37.95835, -86.77832 37.958...  \n",
       "3         207  POLYGON ((-86.78400 37.97424, -86.78236 37.974...  \n",
       "4         347  POLYGON ((-86.80715 38.11784, -86.80709 38.117...  "
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Now, let's read in the voting data  \n",
    "VTDdbf = gpd.read_file(\"state_map_files/in_vest_20.dbf\")\n",
    "VTDdbf.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "4f3b9ddb-b971-472c-a353-521377f95e46",
   "metadata": {},
   "outputs": [],
   "source": [
    "#VTDdbf = VTDdbf.to_crs(tractPopFile.crs)  #MD's precinct file in wrong CRS\n",
    "#VTDdbf.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "3fe4ceba-8077-4485-ad0a-f8f2a5755040",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "5166 0.5819819637963837 2972355 = number of precincts, statewide GOP vote, total Trump+Biden votes\n"
     ]
    }
   ],
   "source": [
    "# Pull VTD geopandas data columns into arrays\n",
    "vtdGeom = VTDdbf['geometry'] \n",
    "# vtdGeom = VTDdbf['geometry'] #can't use VTDdbf because it uses alternate coordinate system\n",
    "vtdTrump = VTDdbf['G20PRERTRU']\n",
    "vtdBiden = VTDdbf['G20PREDBID']\n",
    "\n",
    "nPrecincts = len(vtdGeom)\n",
    "stateGOP = np.sum(vtdTrump)/(np.sum(vtdTrump) + np.sum(vtdBiden) ) \n",
    "print(nPrecincts, stateGOP, np.sum(vtdTrump) + np.sum(vtdBiden),\n",
    "      \"= number of precincts, statewide GOP vote, total Trump+Biden votes\" )\n",
    "vtdTrump = vtdTrump.to_numpy()  #these two lines try to avoid pandas complaints when we overwrite precinct data\n",
    "vtdBiden = vtdBiden.to_numpy()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "c5c57f52-b8d2-4ef4-9d02-2429ed5e59da",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#ID the non-polygon PRECINCTS\n",
    "notPolyVTD = [0]*nPrecincts\n",
    "for p in range(nPrecincts):\n",
    "    if type(vtdGeom[p]) != type(vtdGeom[0]):\n",
    "        notPolyVTD[p] = 1\n",
    "        for geom in vtdGeom[p].geoms:    \n",
    "            xs, ys = geom.exterior.xy\n",
    "            plt.plot(xs,ys)\n",
    "        #plt.text(vtdGeom[p].centroid.x, vtdGeom[p].centroid.y,p)\n",
    "    else:\n",
    "        hi = \"hi\"\n",
    "        #x,y = vtdGeom[p].exterior.xy\n",
    "        #plt.plot(x,y)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "ea498c6a-d054-409e-92fb-cda921e2911f",
   "metadata": {},
   "outputs": [],
   "source": [
    "#prep for geography triage\n",
    "#isSkippedTract = [0]*nTracts  #done above; already skipped two lakeshore tracts\n",
    "isSkippedPrecinct = [0]*nPrecincts  #will be used later"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "3ded1b1f-5629-4d8f-826b-e037e5dcb3da",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "working on tract 200\n",
      "working on tract 400\n",
      "working on tract 600\n",
      "working on tract 800\n",
      "working on tract 1000\n",
      "working on tract 1200\n",
      "working on tract 1400\n",
      "working on tract 1600\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#build basic IN map\n",
    "#tractMAP = tractGeom[0] #uncomment for first time thru loop  **************\n",
    "for t in range(1,nTracts):\n",
    "    if t%200 == 0:\n",
    "        print(\"working on tract\",t)\n",
    "    if isSkippedTract[t] == 0:\n",
    "        uncomment = \"if-redone\"\n",
    "        #tractMAP = tractMAP.union(tractGeom[t])  #uncomment to build map  ********\n",
    "x,y = tractMAP.exterior.xy\n",
    "plt.plot(x,y)\n",
    "pt1 = Point(-88.2,37.5)\n",
    "pt2 = Point(-87.45,37.5)\n",
    "pt3 = Point(-87.55,38.8)\n",
    "pt4 = Point(-88.2,38.8)\n",
    "clipPoly = Polygon([pt1,pt2,pt3,pt4])  #clip eastern outcropping NE of Philly\n",
    "xc,yc = clipPoly.exterior.xy\n",
    "plt.plot(xc,yc)\n",
    "\n",
    "plt.show()\n",
    "wholeMAP = tractMAP  #in case we later slice parts out\n",
    "            "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "f33de347-5473-4b11-b3ae-86691b33098d",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "I have flagged  66 tracts w pop 204682.0  to reassign to tracts...\n",
      "[177, 331, 398, 399, 401, 748, 832, 833, 834, 835, 838, 908, 1274, 1275, 1413, 1688, 1690, 1692, 1694]\n",
      "I have flagged  184 precincts to reassign to precincts...\n",
      "[479, 480, 481, 487, 488, 492, 497, 498, 499, 509, 511, 513, 515, 519, 520, 521, 541, 882, 883, 884, 1122, 1139, 1143, 1146, 1147, 1152, 1154, 1157, 1184, 1188, 2251, 2253, 2256, 2258, 2261, 2262, 2264, 2265, 2269, 2270, 2271, 2273, 2274, 2279, 2280, 2281, 2282]\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#find all tracts and precincts centered in this clipPoly, flag for cutting\n",
    "# and, ID all nearby tracts and precincts to receive their pops and voters\n",
    "\n",
    "cutTractList = [-99999]\n",
    "cutPrecinctList = [-99999]\n",
    "tractReceivers = [-88888]\n",
    "precinctReceivers = [-88888]\n",
    "popToCut = 0.\n",
    "for t in range(nTracts):\n",
    "    x = tractGeom[t].centroid.x\n",
    "    y = tractGeom[t].centroid.y\n",
    "    if tractGeom[t].intersects(clipPoly) :\n",
    "        CP = Point(x,y)\n",
    "        if CP.intersects(clipPoly):\n",
    "            isSkippedTract[t] = 1\n",
    "            popToCut += tractPop[t]\n",
    "            if cutTractList == [-99999]:\n",
    "                cutTractList = [t]\n",
    "            else:\n",
    "                cutTractList.append(t)\n",
    "            x,y = tractGeom[t].exterior.xy\n",
    "            plt.plot(x,y)\n",
    "    else:\n",
    "        if tractGeom[t].distance(clipPoly) < 0.08 :  # and tractGeom[t].centroid.x > -76.4 :\n",
    "            if tractReceivers == [-88888]:\n",
    "                tractReceivers = [t]\n",
    "            else:\n",
    "                tractReceivers.append(t)\n",
    "xp,yp = clipPoly.exterior.xy\n",
    "plt.plot(xp,yp)\n",
    "\n",
    "for p in range(nPrecincts):\n",
    "    x = vtdGeom[p].centroid.x\n",
    "    y = vtdGeom[p].centroid.y\n",
    "    if vtdGeom[p].intersects(clipPoly) :\n",
    "        CP = Point(x,y)\n",
    "        if CP.intersects(clipPoly):\n",
    "            isSkippedPrecinct[p] = 1\n",
    "            if cutPrecinctList == [-99999]:\n",
    "                cutPrecinctList = [p]\n",
    "            else:\n",
    "                cutPrecinctList.append(p)\n",
    "    else:\n",
    "        if vtdGeom[p].distance(clipPoly) < 0.08 : # and vtdGeom[p].centroid.x > -76.4 :\n",
    "            if precinctReceivers == [-88888]:\n",
    "                precinctReceivers = [p]\n",
    "            else:\n",
    "                precinctReceivers.append(p)\n",
    "print(\"I have flagged \",len(cutTractList),\"tracts w pop\",popToCut,\" to reassign to tracts...\")\n",
    "print(tractReceivers)\n",
    "print(\"I have flagged \",len(cutPrecinctList),\"precincts to reassign to precincts...\")\n",
    "print(precinctReceivers)\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "33897c0d-3254-4a76-9b82-314a8f320eaa",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#clipPoly block 2 of 4\n",
    "#visualize the to-be-cut precincts, just to confirm there are really xx precincts up there\n",
    "for p in range(nPrecincts):\n",
    "    if isSkippedPrecinct[p] ==1:\n",
    "        if notPolyVTD[p]==1:\n",
    "            for geom in vtdGeom[p].geoms:\n",
    "                x,y = geom.exterior.xy\n",
    "                plt.plot(x,y)\n",
    "        else:\n",
    "            x,y = vtdGeom[p].exterior.xy\n",
    "            plt.plot(x,y)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "e2595c62-579a-4af1-9552-0859524782be",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#clipPoly block 3 of 4\n",
    "#now visualize the receivers\n",
    "for pp in range(len(precinctReceivers)):\n",
    "    p = precinctReceivers[pp]\n",
    "    if notPolyVTD[p]==1:\n",
    "        for geom in vtdGeom[p].geoms:\n",
    "            x,y = geom.exterior.xy\n",
    "            plt.plot(x,y)\n",
    "    else:\n",
    "        x,y = vtdGeom[p].exterior.xy\n",
    "        plt.plot(x,y)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "9e1d7fcd-cdeb-4317-9f89-5291a9595587",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#now visualize the TRACT receivers\n",
    "for tt in range(len(tractReceivers)):\n",
    "    t = tractReceivers[tt]\n",
    "    if notPoly[t]==1:\n",
    "        for geom in tractGeom[t].geoms:\n",
    "            x,y = geom.exterior.xy\n",
    "            plt.plot(x,y)\n",
    "    else:\n",
    "        x,y = tractGeom[t].exterior.xy\n",
    "        plt.plot(x,y)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "d2622568-1fc9-4089-9ae4-2550ea6fbaaf",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "I have finished slicing  204682.0 people (VAP of 159530.0 ) and  91917.0 voters out.\n"
     ]
    }
   ],
   "source": [
    "#now, cut and redistribute\n",
    "cutPop = 0.\n",
    "cutVAP = 0.\n",
    "cutHisp = 0.\n",
    "cutBlack = 0.\n",
    "cutTrump = 0.\n",
    "cutBiden = 0.\n",
    "\n",
    "for ct in range(len(cutTractList)):\n",
    "    t = cutTractList[ct]\n",
    "    cutPop += tractPop[t]  #sum up the cut pops\n",
    "    cutVAP += tractVAP[t]\n",
    "    cutHisp += tractHisp[t]\n",
    "    cutBlack += tractBlack[t]\n",
    "    # Now we zero out the pops in the cut tracts\n",
    "    tractPop[t] = 0\n",
    "    tractVAP[t] = 0\n",
    "    tractHisp[t] = 0\n",
    "    tractBlack[t] = 0\n",
    "# Now distribute the pops among the receivers\n",
    "ntR = len(tractReceivers)\n",
    "NTR = float(ntR)\n",
    "for rt in range(ntR) :\n",
    "    t = tractReceivers[rt]\n",
    "    tractPop[t] += cutPop/NTR\n",
    "    tractVAP[t] += cutVAP/NTR\n",
    "    tractHisp[t] += cutHisp/NTR\n",
    "    tractBlack[t] += cutBlack/NTR\n",
    "\n",
    "#             now do the same for the precincts\n",
    "for cp in range(len(cutPrecinctList)):\n",
    "    p = cutPrecinctList[cp]\n",
    "    cutTrump += vtdTrump[p]\n",
    "    cutBiden += vtdBiden[p]\n",
    "        # Now we zero out the pops in the cut precincts\n",
    "    vtdTrump[p] = 0\n",
    "    vtdBiden[p] = 0\n",
    "\n",
    "# Now distribute the pops among the receivers\n",
    "npR = len(precinctReceivers)\n",
    "NPR = float(npR)\n",
    "for rp in range(npR) :\n",
    "    p = precinctReceivers[rp]\n",
    "    vtdTrump[p] += cutTrump/NPR\n",
    "    vtdBiden[p] += cutBiden/NPR\n",
    "print(\"I have finished slicing \",cutPop,\"people (VAP of\",cutVAP,\") and \",cutBiden+cutTrump,\"voters out.\" )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "fe47f683-719d-4a02-9648-905bddbf28a3",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Now, repeat for western MD\n",
    "\n",
    "x,y = tractMAP.exterior.xy\n",
    "plt.plot(x,y)\n",
    "pt1 = Point(-80,40)\n",
    "pt2 = Point(-80,39)\n",
    "pt3 = Point(-78,39)\n",
    "pt4 = Point(-77.5,40)\n",
    "clipPoly = Polygon([pt1,pt2,pt3,pt4])  #LEFTOVER FROM INDIANA. plan to clip off SW corner near Evansville\n",
    "xc,yc = clipPoly.exterior.xy\n",
    "plt.plot(xc,yc)\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "cac3531a-35c7-438d-b5b9-5fcdf0745329",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "I have flagged  149 tracts w pop 201388.0  to reassign to tracts...\n",
      "[557, 558, 564, 565, 566, 568, 569, 570, 573, 575, 576, 585, 664, 668, 818, 825, 826, 1524, 1525, 2522, 2524, 2528, 2981, 2984]\n",
      "I have flagged  92 precincts to reassign to precincts...\n",
      "[649, 650, 652, 658, 664, 670, 672, 673, 675, 676, 681, 690]\n"
     ]
    },
    {
     "data": {
      "image/png": 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70TbbgAr0PuPorFzl5eWntX31L89ix0Kbac80+nrvAcPR37OYAmMcA9b8lvVfvnTaZT2ey+ki/tAuinsOQKc7819H94L55IbG0ndMCll52mdSavpF+5m/ACklG5ZNAS/0CnwRUZuPJCNtIwjBFY8/02g5ls/6GtDR87KxBBbFYO5dTfEH22jjiMQ8qQ0m84k9LaSULMkqJV6vJy723Ca4ev+XjTikgTtHdjrlul3bduX6iOvReXVcN/c6jhQfOen6m/et5oClkCqzC4vFv255dcZaYlc/Suy+99G92ZOcx7sQ483E5Wdpcl/2wgK+ve9OanSCwWMuQ29oeaNCqU07rwFBofWWV7uq+eWwdq7v6X1P3fJhffqj719CQGZbUpetqbfNoW1FzHttMxnPr2Pvc2up+NpK4TptsJPXqSfnuwnY/Nwc0m9n2YaRfLV0JLEymzxdR6IC4nl46cN8cHgHFp1gZHhgi4/lfFGB3kccDfB2u73F2+Zt/oW4ynTy288gMCK2yfWi2iYR/8flbPcfTMquf7Jvw/yT7rfE7qK0xlX3XErJnD15fLojhyWHi/F4vezauBOLx0lo/74tLndDuXsPkpCzj4oR4/C4nHyYn8S0qHyG9P4aJBySW1iytBOuMDuBJfEY40fxhz9/RM9H5vBp+Eiq/UMJ7dn7hP1WlVeQu3ctwdG92VepBcVIfwgt0Zp8ojebcFeeMMEaq1dmstnh4OoO53bGNK/Xy7fpBYQa3Ewf2q1Z2zw99Wmuj76ecl05i/YuOum66/Yda4Y5/iavK/27usc1xkji/QtJGFOCt4kvtbLdu/ni/rs44rLTu3N3et//u2aVtaGqGm0K60BL/fcx6U11j93e+lcVt99yBeVBBez8poSsvGNXMcs/34t/VgWWChcBdm2bdmUdqClJpLoqjSp/Axa7h7jQP7KV/nikm0O0pzLiTt4Z/w4mvZlFxVW0N5RfVEnMGlKB3ke0b98egLi4uBZtJ6XEufDv1GAh7uq/nXJ9k38Q7e6ejQsDbX+6A3t1VaPrzfhiE/3+toC+zy2g499T6fnqErr8YyGPfryFp2elcde76+jx2lLWvfMRAF1GprSo3I3Z9snXAPS87Tr0Oj1edLQxuwiI7M/YUbuo3hHCO2l38cjiv/P6pl9xy4s/Mc8bTYR0sCegMzvjhvLR21rm7ZzDh0j95ivcbhfLZ30L0sngq67i5w1LAYjbq6Na5wDAmVVBwUsb8brq37j7emM2AHuKKs/42E4m/UA2WQ4/DDpadFXUNrQtAHP3zT3pereM0mrHQ7zd6nUdrIk7dk8iwH1snue4GbfjaXBlWbp7F7OfeoQyHYy//Cou+8fLzS7n8Rau/YZN7/wXAD+jf73XDDoDNyTfAMCnuz6t91qQfyCT7u6FzmPgy5fWsWnHDjxuL9U2Fwc9x26Em3oKzEYzsTs6YQi1UhkcjsegY6EtmlfFX/jW+CRPiVcZ3nYoSSFJ3DXgH3gNkXQ2XtzzXKtAf4nL+uVNEmt2Utz1VkzBkc3aJiIikgxzJ5wYWJpXP9B/f6CQwTNXsTmtgKj4IAb1bUNkpD8eKTH46bliXBIPzujOhBEJWKz5jNqizRW/8ZuX2Lb5F9yek7fvArhq7Gz84m1Sbx5P6rSh7E9bBoBp2UIy23SkU59kds97AS867FU2dqRvZu/iRfz14CPsyO9BmSeE9bpodvnHcEdAKVOHae3nUaKK3PIKtq1ZyQcffMDa7btYNX8++9YtxOQfT69xA5g+fjyRBkGnmnjyBtegD9FqkdLlxZVXP6A/cEUyAAvLKtmRXtCsz/Z0XPffNACsTgMO16k/v6N+PPAjACWekpOu993q2QBc2e3qumXS62XpT0vYYWtL1RUz4fbvcAX3oTLPTOlLr7J/7Dhy//IXbKkL8DoczH/9n9QYdMx44I/0/dXdLTxCzf4D29j61oe4zAJHjwj69D7x5nd8YDwAOnFiaKssX8KWnh8g3HrW/Sufdx9chvRK2nYOxXRzV7xSYs23oA8uItw7DQC70Y5bD++429LBWcQahz8hspDBUUn8vCedFzflg5T8pceE0zqm80X1uvERpzNIoyxrJ9EbXqDQlECbGS+cdN2s3L3k7viJiKRBlGdvo79zD+8xlZdnbeGmUe0J8zfx0/Y8DmWUInSCvj2j+PzGAfgZmr6cfSJ4NwfXQZsyQZc5m2HOZrabwBplweSBqgA93gALHiC8oBqLrYYas46Aag+BTnAHCsxOyZE//gHP396nbUkOh27WenDED7kW/ZYDfFrag08/y9fe0BLKb9vlcs9N19P35VUA/Ormsdw5az2hBh2/u2Eyn339DfMWLAaD1k5rPVCAx1lC7wkzEELQP7knuaZ9VOFkyKQJmKdbyH1hPV6bE2Ob+l3runSN4u/9E3l6SyZzVxyiZ++YFp+j5ugU7GW3TU+gzoXZeOo/aWuFlYLyAvbJfQD46f1Oun7qoflYzHomD5xRt2z/xrXk7N1Lz/veImDgZQAY/7gC8vJIzM2l7Os5VKQuoPybucjwMHLbhQOQMGZ8i49v8bp5rPz+M8yZVegEXPPcC3RP7NvouhllGQD0iOxRb7mrupjHd86kKkCwp8+L9M0dT5887Qb6kGFOYnpHsWP+YYKLqjHcNBzXnFQApM4LQlCFkTK91gQnC19lzIc/cXj/WBxD4jHYnCQGXLw3YkEFep8j5Sk6CNfyuF1Uz74df7yYb/ofemPTw7ZlVTER/x1Dgqcaau9l7QnrRcy45+CrfXyWqv1xSQE9ekUxc2ov4kNOHjwA9rsy+PEuE+tvWoMzN4edP35K6Z7tmHOs2AyS4FInwlah9WEPNyN6xKOrdlDubyFw3BiGXnkvaz97ndiXZlP48H2ECx2pUT2Y7PUSktCDOTPKWL/nEHuOWPnW1hmAveWS0PAQ7g0updIlKXZ7OGw3cU0XP7r07I3u00/x+mkB2+R1k7VuISb/OEbfcoX2ua2voY1RYI2xYDKbWTJ/Hza9hxQAw4lfttNGJfH0lkxCQs7dsPjPfj+Z/v9YxPC4U2dMtNltXD3nasp0ZXXLLou5rMn1y8us7DHnMEDfjV2LFuJ2OXHa7excvpiw2Di6jx5Xb31jbCzG2Fj8BwxAev5B1Zo1lMydizFrDy6DnrL8PELbNH0fqKH/e/shnGsysOjB0z6MgZOvbjLIA2RVZAHQPbx7veUZGb9QpRPcHdqLlPiRpEWtpGt/DyM2fkThxpth4GDa39Gdwtc3UzDvAB0eHsf+9OfQC388VNOdw0TJA0TKAhbVpFHkjiKszQDygpPoXtG8v7kLSQV6H9HSGv3B2Y/Q2ZFB9oAniG9/wkyR9ezd/gtdPdX8POx5Qq27sSeOYPSQ6+mq1zOsYzyb8m1UOdy0D/VnUGzzazbZZTswWpIIMAUQkNSFEQ/+vUXHADDy9idYO/NLokqr2BidzHeZNYT+uIvnpvWk38DhdOs7hAfe+RZsEE0p943pCMCTf74VgFvf/Bk9koeu6A/A4IEDyThwgCK7A1lqRXqrmXTfU+gNBvbP2YffpgI2hRt4qpuF0iVpeMwCBvmTegDaHlcur8fLe7O2sd2qNecknMNeN2kHcpEIwo2uU66bkZtRL8ibPCbuG3Vfk+v/uOlrXAZJ5G4Hi3/5T91yo8WPqQ/9CZ2u6Ss2odcTOHIkgSNHcuvePcx+4WnmvvQcA6+8hh6jx6M7xc3LrdtX4ll1AGdiAPc++gZR0ae+/1RYXYhO6Agw1b+6atd+PG02v8QHZdvJd5Tx/IzvKV69ALPuHUwdBgIQEBOAMzmc0H2lZP2ylXLXEEKDNxL2jYFZL4bw1c4P8dfBIm8go2Kn0qvnRP52OJ8RQRfnIKnjqUDvS5pZscjdPJ8Ohz4lN2QA8VOfOOm6Xq8Xue4/FJkiGDP2XvyN9WuNbfzNTD2NXiWbivZTVbWHjm1OHJjUEnq9AT0GwE3E9Csxler4bH0Wz1zRnX/O38PHqw/j9lrQIZl9/2g6Jx7re30ov5Q1eV4GRbhJjAkDYNK11+P3r3+xxO5AGk1kdJqAKSycqjI7Wzbm0M1g4PleFhCCu91mfsRJjgmCru9S70boNwszeGm31oe+s8HA6JSW3SRvLofLw4J0rRbr0TfdrRHA7XHTt31fklYlcZjDzBw2k6Gdmx785qyx8/X6TzDF6OimT6IisIDbX34bv+AQ9AZDiyoX4cldufx3j5L6n/9jwXtvcWRnOpc/+MgJ+yjPzmPrZ98R37sXi9JnIZD86pEXmxXkoXZ0dCMCg2L57oZlvP/DHXxgz8T1+TgeLO9BhNQRNmRi3Xodb+3G/mfXsjfdTV7FrzHWhBK7dQmF9/yZ2D9I9rt0vDLsfS5PTuH6tAxElYuvNx7iN/3bEduMq9gLRQV6H1Fd5iaycDi/vL2HHmPKGDa5JwajgQpbJd++u4aaCg/RSUGMGx+M7puncZtNRPxqdr08Io0pLNhPt7IdrBn8Z4YZz95kCk9ueBfQ86e+vzrjfaX19jBmOcQPbMc1RW34YuMRHv9mO3O2ZKPXCW4a1I57R3UkKbJ+zeuxT1cCgj9MOdal8sCmTSyxWonOL8BV7eDA9GF8P/trtnrbsVRYGJAcQbZF8FnbtoztGkNN6k4+wUVCYP0/8h+35OAP/HjHYJI6h6MznJt+D+Ne+JEcu/ZnfLis6Rr9rLRZvLSt/tiHlQdXnjTQ11RUkB9RQ1urhfG33cMXf32cdXO/4LLfPHhaZe2UMpiOH3zG2jmfs3bOZ8R26Uq/SVPxujz88Je/U2zNpsZZid1VyebNP6IDKiP1xMd0aPZ7xAbEkl2RTWlNKWEW7ct7a/r/mL/nK7oExDMycRyOjJ/41FNIO/dP3KrvSkRoeN32BpMey+QEDs38HKGPpCB0KJ1Ygi6zmrjX2rPl8gGMmdgHm9vD2rIqdIU1VDjcLNtbxE2DEk7rczkfVKD3EUGWMIQ8grdKz/afSkhPXUSPcdHs3ZyDpzgAr8FDdpGT/220Ai8xOHElKWGnbistLDhAG8AvqstZK+vqwgzyipcSEtSb4dGnHuBzMi5XNZ8PdnOgl4Nb1r7GvXfPZs7+r5izRQtgz13Zg1uHnjgqddv+LLYUC0bH6RncQ2vO8bjdLJg7F7PXy8iVK5n14KN8uLEGGEAfYz6LR0SzOsDIr7Ewtqt2Y3WucBLkAh2So3lxpJRsttkZHRJAh27N68nUUku3Z/Lol5spdh9r+z9Y1niPGyllvSAf4AkgwZDAlb2uPOl7BEdFEyoDqQpyERAahsFkorzwzHoPCSEYOuNG8g/sY9knHxCVkMSWT+eRkblJe09LJOOvu5c1WSspXLOO7Pimc800JsGcxEaxkRee+5To6gQ8FgclopxtbY18FrIarKvr1t1hNuEIOvH3eveOVXjdmeDOxFndDVtQEsEVh6GsHIPNwtOvvcuwu27BJSWmwhoAbkg5cZTuxUQFeh9hqO3dcsWDvcg6fIT0xbnsWlAGBJA0xJ/Lbx/D528somy/dsrXZ44kfvlC2oxu+kYcQM0Wbbi82T/srJV1U/FhhHRxTefrznhf67Z+QKlez+RqB1GOLGZ89yB6v2DcZdrra5dkck3ftvj71b8a+WjZbrwI/jxjcN2yxf/6FwUmEykbN7Lnnvtwtisis6gTidWSjq423HPExb+6GglopyUvq3J5qDAK2tTUbzNbtCaLSiSDE8M5FyrsDh74fBvVXi3IG/HQJ0rQsYmRmUII/j383/x29W+1cuurGBI3hG5tTz24akzbUcwu+4Gn/3kzyfpoRt78qzMuv9DpmPLgo3z21CN8+ddjTYc3PPw88UP7AJDMWEb4D6VG52Tr3g30Sz4xPcbeXSt5c/Hf2WTOw+KCEJeRnCA3GMAvXAfhVVApSSzoQfdqEyN+14c8RwYHi3ZQZs3kmiOpLLBEsGvxXqJMBjoEWgisKObw6o8x+bXD4B9OXMoPWC/Lx5qu5zuTP5lRa4mojuPBJQMg3IwoczKua7SWEfMipvrR+wztF02v1zNqan9+8+JEOo72o+eUUKbcMRidTscND41jwn0d+M3LAzCLKtb/lI10e06+23Dtsrn7V9NZt/ids1LSSJMWoPwbDHg5HT/vn0ewV9LGnUCpPphq/U6cSUk4hkThibLwU0UFd755bNh7ia2SorIKlh2uJt7PQ+e2EQDkbN/OuuJi4o8cwVjjImnyAG7aH0JitcRdchCACXu0n58eyafMXsP6DG2QzAuREfXa5x/8bgc6CddMOXtXQcd77otVVHv1dA1yclUnE/tfnMacR67kpTub/tIe2Wkk227bRke0q5fNeZub9V63jtG6q2Z28nD7y28T077jmR8AYAkM5KpHn657PuN3z9UF+aM+n/w5od4gHl31GJWVFXXLS0vy+PMbU7l+/f1sMucxuiaRwaIDkRU6OuR7uTmrL3977EEee+wWnnjuVkYMtlLl7Mzi10uxLJfcMfZFhkXcRju3h6WJHfifzs5r7goeKCsi7Kub+G2X9dybOI/Y8AN8VDGEiH02nugiWJtUTG5ALgeDcvFGWvArdRJkNvDurQPOymdyLqkavY9w1tS/bDebTUy+qX77q8FgILlvEgD9+1WxdksiuZu2ETekf5P7HTT97xwZcCO2uffTf9UzHEgeR8f45g2zb0q1R/tyCdCfWT2jurqYxc4iplja4vQLpmPZKiAKry4IGWjC1T8CtpeyPreSDetW8+aifayujK7d2sT9KVqzirO8nG8++gijycSATZvZcE8vDDm3Qh+QC/9L9fp/YYzowk8DkoGelKLjT2s209ug7atHklZzry6tYdPM7Txgs1Cqk8z9+QBXXtGRiJCT3yRticyCEr7bW0WPEC8/PnFVi26IHjp0iPGh4zlQdoB9zn3kWHOIi2z6JqfD4+Dx1U9gMVh48aZ3CY6MbnLd0xEeH8/I3jch8x1Eh5zYvt0uLoknu/2Jh/c/zqr0pUweNo38vAzunnMDR0IdTK3pykPT/kl0nNZ1dusdj2DctJguq/5Vbz8ZkX1xGzLBbWbz9ihSvF6qc7/HKQy8Pv4aDMYIMkqr2bjzFwZGZFPtGY7NfRspMp6UPHjb8wr3rQ3Fz7aGThmzyWobxKMP6fjPhO5Mvuni7j9/lKrR+4iaSu1GXGBY8/prRydoNw/tFSfmaKlHCNq160nsrbORCKzL/+9MiglAqVPLxxNuOrMa/fJNb2PXCa7oegNtJj1CiLCz/vARLObeJNrzGVzgRleovdf135bVBfkEXSk99XlY0xbzySef8P6TT1ISHMyAzZsxmmvo3EG7Agg5MgZP0UGEoxJ37hYKIgp5fPWPxNRUscBt4IU8K7q8aka/tpykJ37irT+vZvuhCvx0gmB0VK0o4MMnV7PvUNkZHefx3vo5DTc6nr26X4uCvM1m49NPP6V4azE9SnpQo6/h7Q1v4/Q0ff7f2/Ye6UXpPD/8ebpHdm9yvTOR8tiNtO8+gJLP9+I4eGJCvsF9RqKTOtKObCHzUDp3fHMd+RYn/xf7e56/f05dkAegogyHJRRT2LHgm19Yxa5F2RjceiRuevrP5+BfxzHw0LekJUwmIrwtIUFmOoW4+M26xyl2/YUS15O4ZXzdPkYM/Ax/HdiDhxFpg9W9U7BIyajwc5uo7mxSgd5HHB0npW9m7w6nU1tvy88ZzVo/PCKBrKAOGMszT6t8R3m9XpZkLUCip3vomfVS+PnwAqI9kgG9b6dt96G4jMHsCexKuTGErJowtqUVINzaB/PXnlbSHhvImtC/8p3pSa6KzENKL4cOHaIoOhqj00lYRDYHn4qjWlgwVcYRYrsVWa3dJLT5Qd8Cf8pdLnrmHcJb6MSUVoIpvbSuPNU6ScdYf3799hgeensMYaPb4O8VrF6dfUbHeZSUkiUHKki0OBjUtXlpj6urq0lLS8Nms2EwGPD39+fe/veSJJL4qegnpsydwhd7vjghCVi5o5zP9nzGpKRJTEya2MTez5zOpCfyzh7og4yUpx4+YcBfYEAQfUQ3Ztd8w81L7qHMLHm70xOMnXzPCfuSNTVI47Grp/2HyvjmmfWEaymJsOv1ZBqy6ajbir/HTk38sbkPjK/eSb7jXWq89a+CrSbJB+Hj8DMVEVJ+AIDVfQYwOjIY/zO8Ij2fVNONjzj6ByKaeVOo/dQr4OcluGXzukxWV5eRWHGA9XHDzqiME356iKKSZXSMvZoOwaefEqCs9BCrvDZuDeqETq8dg/EvR+i2+xd65lgZGlzO14F+ROqcfHvPUCIjtfeqHP4wYYt/yxX929Fm1B1YrVYcDge70t7ENsGJwxBGuPswr5dZmX17bzLf+S0ek4lAh5uxv2j5zL/qMpYN3bUbspEGM3luN//olcjE7tFE9zvWvNG3XzRLl+fj9bSs50hTlu/IpNRt4LqewSe85na7ycnJISoqCn9/7UrJ4/HwwQcfUFJSgl6vx2Qy4XQ6GTt6LGPkGNbmreW9be/x/Prn+WLPF/xp4J8YVnt+Uw+nUuWq4tc9f31Wyn4yOouBoLHtKPv2ADU7i/HrWb+n0tXtr2broZ0EewJ5ruIuBo29pdH9eL0SnTz2hTXng3SCgRKdl3CvDp2Eb6zDKWvrxwOG73nSmcibO3bTNSGRMtexm8KlunL0Qk+wJ5CZHQTr9cMYZVzKkEPL2NK3H4XhkUyMOPEcXMxUoPcVtRWh5l7NCyEwCBfhQRWnXhlI2/gNw6SbsB5TT6t4Hq+He1e/TVHJMrq0vYrPxz17Wvs5asHGt3ALwRW976y33K/b5SyqvYXw90byTDl2peJFENH3coQQREVpg73Stm1B54gEPwteFxj8u1H5vpZZU+904jdkCPZ16wCYnrGSj7pN4f+mt+fv32cRrtdz6y0nzh61ebPWFbFL14gzOtajZq3cgx4vd4yrf9PS7XbzwQcfkJ+fj9FoZNCgQQwfPpyamhpKSkro168fLpeLHTt2kJKiZQkVQjCs7TCGxg5l6ZGlvLrpVe5ddC8D2wxkQMwAFmVqqYs7hZ1Z99fmChgYS9W6PMp+PoSlazjiuCvTQYsP8Q/Dg3Sv7kC73/TQMq4ePIghIgJ9aCgA1fklWLJ24ugzum47fZmbcjM8/eYEXA439//mDba07c4WdxeOhI2l2BvG9PwqnlmygCvQzlGlrYDNxfN4e/JurssfxGE5hjtWH8RTDWl9H2Lj2CyQkjHBZ96R4HxqPdceyknV1eibH+kJsxRzoKgD79y3hG8f/ZCsxcvx2KwnrOr1eojZ9G8yA5Lo0b3xmZROptzpYOJPD7H+4H8JDh7Ep2P+jOkMc3f/nLuSDh5BcueT9wU/npSSsILVFPh1wRRy7GrCbq/AbM7CZOpHTEhPdAJKK/YQdvvtx9ZZt47Qm24kbP4iHp/0GNemtOOqYT3Q6wSmRkZjOp0eslfnU6z30qfnmeejd3u8rMl2kBzkIi66frfNvLw88vPzGTx4MN26dWP16tW8/PLLrFu3jsjISDIzM5k+fTpPP/00U6fW/6IWQjAuYRzfTv+WPw74I8X2YmamzySjLIMBMQMw6s7eILmTEXpB8OT2eEpqqNpUv6++f+8UBlR1J6SdHUuHthS/9x4Hr5hKxqTJOA5qPaH2v/M5eq+ToKtnsHn+YdYuzSLAA1G9tM/KaDZgdBy7B9A9MIJlXeJomzeTjwKPJfTbSDj2kMv5szseSSzDtq8lLCcXnXUdUnrZHRVKZ1lBXCtIe3C8UwZ6IYRFCLFBCLFNCLFTCPFc7fI+Qoi1QojtQogfhBCNXssIIUKFEHOEEHuEELuFEGd3wlEFOG4S5RZ0553yh+FE+2ntxzmVSfzwtYeZf9pC5sL6E1Fs2/QNHSsyKBj8O8RJcps05cbFT1NYspyeCbeyYvr7+BvPbKh4Xt4WNgsHUyJ6I5qZf925fSXWpy8j3GulOqR+ZsMDB5ah03nx+sG+I9q4gQC9pDLYSLc9uwn99kcO/fVNNk2/mxzhhzU8ltIq7Sam9EoiGskYuXpJFsFewWqzm95/W0ivJ35mzFMLuP75Zfzh7XW8PjuduYsPsm2PlerqU+eo+X7dHqq9Bqb2PnGQW2RkJEajkcrKSq655hruv1/rErlhwwYCAgIoKSlhzZo16E/y5WrSm7iz5518d9V3rLt5HbOmzOKd8WenO21zWZLDMCUEUbH0CNJzrK3e0kOba8Fb4cJVUIDz0GHteXk5BS++iNfrxb3gO2oiEsmsjGDdtwfZ8qV276nmuF+P3z52rMlnxrWjcb67hWs8pVTp7fxf8sesrnRj84LFYGFGznJMaRvxyzlA/wF9EXgp93OQKZIYHbjr3H8YZ1lzmm4cwDgpZaUQwgisEkL8ArwNPCqlXC6EuAt4DHi6ke3fBOZLKa8VQpiA1nXN00pIbwtr9EBgQnuuebEtucuXEdGzBwdSV7BifRsOrM8k8bKj+/UQsvIlMv3b0W/YHS0u18vpc8ku/IXkuOv5fOzjLd6+Mb9s1rrPjUq+iY1rZ5PYZRThDheOr/+B8FSBoxxqKsBZgXBVoddXYPJzEGUAu9VIW8cP5K35kthh2iQVBYVrAdDXLCRaB4tsBg45dBRUF5Ce5ebBLzKoqHFDWhoARr3goQmd2XWghCK8TEs4cTDZ4aU5AEzoGEEvvOSV11Bgd7K/ws5GWxUypxi2H1vfTwqC9DqCDXpCTQZCzUZC/Y2EB5iJCjEze0s6Bvy5vGcibrcbw3FT8Pn5+TF06FBWrFjBwJSBJLVP4qmnnmLevHns3LkTgKqqxieIaYyfwY8+UX1OveJZJoQgaHQ7ij/dRc2eYvx6aG31ls5JeGv2YN+WQcbopwiZcQ3mbt1w7N5N1YqV7PzTy/iXH8F7y+9J7BXJvg2FSD89XrubT3fn8tHfi2gbauE3Izsw2mRjmTOYOR8t5+CY/YwK2sKmQjOrK3fSvvbGfbU3HLc04QywYKqqYe2mxQQCh5N3A3H0di0Bmk4EdzE6ZaCXWpvA0RkVjLX/JZAMrKhdvhBIpUGgr63ljwJ+VbsvJ3CK/nzK6ZAtbKM/Sm82027iJADihvaH9blEtz1W89u9ayndKzJYO+YVEg0tu4xfkreb/217CZNfZz4e/aeWFawpjkp+zl1Nbynp9OVtGKWHg6s7E1KUR4Cf9mvqdQu8bj1eaUQaLXj0bakK6UXerHWYQ9y0G1WCXPAQxTFdqNwwC7t5G36h4PbCV1vD2RZdQ7vgdhzICeCB2RsA+Pq+oSzbW8ji3YW8cm0fesWH8MKnaQBMG3Li8He/IBPV5U6uGZpIx/71+587nG4OZ1eQkV3OofxKjhRXU2hzUGp3UubycKiyhqqKamrqncsguuoL+OSTmQDopAGj8MPPGEiAfxCBgVpTwpcf/cg9v72TsDYBXHvttQwYMIDi4mJ69z5xisSLkaVrOPoQM6XfHaBmbymGGH+EQYfOEoguLhadeQjl874leOoVOHbvBsDw4ycAlMb2o3+y1lQzcGw7HMmBmPIrOFJSzabsMu4tyIex2g0crcqRyCj5AbdFOPi80s7ijh8y5vD1dDGtJ9MSxapeYxm37hcCS7X7WNvC2pNodHDlgNfO98dyxpp1M1YIoQc2A52Ad6SU64UQO4BpwHfAdUBjyR46AEXAR0KIPrX7eEhKeUL1QghxD3APQELCxZsc6OLV8hp9Q+u+TwOiiR02sG5Zye75uISe3gNblq5gX3keDy95ACGMzBz3GoEnyXffEssPLGev2cQTxSVs7H4ngaUH6J27FPzA3uNJTJPuQxcYjKFBk4515kxcVZuI7msDwB87/p+OIcirI2OMFhzKdofSb3MMf3v5ZRLadWFLprZuiJ+RXnEhDEwK57FJXev2ueJgMVFSR+9G8tkMu6YTP7yVxoG0ohMCvdlkILlDGMkdTp5WwunyUGC1k2+tYveBI1iqBCYZT2VlFVXVVVTXVGB3VVBeVoAs94IU+DtimfvqFqY+0IeY9sF06NCBDh2anxTsQhN6QfgtXbHNP0z1dityg9aLRhdgIvr+6yh6+yCO3XvwVlRiTk4m/p1/UfTND+xbn8+ujVVs26RNKBMa409ycjSjkrXPvsLhovOanfXea1TmPmRYAoEyjLenvMC/3/ucmsgN7C3KY+feXsx99y565rWlt8zGTxSRFdWByTrw87u489o0plmBXkrpAfoKIUKBeUKInsBdwFtCiGeA72m8pm4A+gO/q/1yeBN4gkaaeKSUM4GZACkpKRd/Jv+LjDzag+804vzGrfn88vEuIhzRhOqzObzwU4I7vU+ey0Ng1go2BfdgsF/9EYA5RVlIr5sg4WVfWSkDuwys9/o9K/6J12PjudHvkRLZ/jSP6kTv5m8DBCm3Lic5uhM//Opuwo6EUXDLfaRc13TK5ZJPZ4EQmO7/AsfXN2P2q6aoxp/vi5NJQpvwuzAtgvjuPWmfoM2NOqh9ODNvG8A9n24mdWc+0/seG0W6PaOEPfYaJkcEN/rlerRLpcly+jedTUY97WIDaRcbyMBeJ3ZFrSytIW3hEfRGQVzPIIIj/cBp4Pu30pj3+ham3N+LhO5np8fP+WROCCbqnt5IKfFWuZAOD7ogEzqTHulyIUwm3MXFGCIiMMXHE/fQ/cQBvXMqydhSiH+Qic4D639eQWYj8RYjPQL96FSUwzsiiLv7dGFCP20O4FWLFoIQ9O3eg/S52whL7onBYMQgJJ6AKLpMvZJFtjJi7GdvlPP51KJeN1LKMmAZMFlKuUdKOVFKOQD4HDjQyCbZQLaUcn3t8zlogV85y1raj/54C2ftwd8l0fUKJqLdYgYUz2XL+7/FXmGld8U+VoYOYOzGveTUaCNPXln9CwN2lJCyy0byzkquzDHidDrq9revPB9r6VoSIkczI/Hs5QHxer3szFtEQFBfkqM7sePmW+i0bjVF1jBS7noCr6fxvD0VixbhKSrCr38/LCljMfxhNS5HADnVIZTZAmmb+yvS3uuKs9LEiBvvqBe4x3eLIcTPyPdpuUgpmb8jn6QnfuLKD7R2/RtGNv4ldnTg2tF7J2eb0+7mqxc2sn1FNlsXHuGH13ay7OMDmAMMXPdECsERFlbPad5guIuVEAJ9oAlDhB86k/aFaUpKwl1YSE16OsZ29WvWEXGBDL6yA73GxDeaZCxK7ybVauMDjCSWHWJC716A9rezZs0adC4HoaFhCClp1y6Z/X/5Bbswk2YP4t29uQiHhz8OSjrnx30uNKfXTVRtTR4hhB8wAdgjhIiuXaYDngLebbitlDIfOCKESK5dNB5ofbesW4HTbaNP21lISJUXY7cQ7n8ghcmPf8z60CkMLviS5H93R4dkY+RA9lY7GLx6O9csSuU1ZyzTqrbzkiUbcfRSQh4LsveteB6km8f6nzh68Ux8fXg90l3EuMTJAITcp90Qc+n17Pnbbezp1YNdv5tMVc6eum2klBT++T6EwUvYwFBcB9LQRSehe3AJ3trLn5DeEwHBqFvvIi65fh4fvU5w7+gOLN5TyLAXl3DfrGPJwPqbLYwdGk9j3E7t8zAYz6wbaVPKi+zYK1yMu7Urd/xzGEOv6Uj+gXJ+fHsbuRllCJ3A5ThFwrpWKOTKqRiiotAFBhJ2y80t2lZfo/1edGIfvyn5Fzq9ntQ5X/Hpf/5FtVfSpV0c2Rl76Rk2kq772xMoQrkfMzcEmzHFBjAhIpiYIN+t0ccCS4UQ6cBGYKGU8kfgJiHEPmAPkAt8BCCEaCuE+Pm47X8HzK7dvi9w8lmoldPS3H70Xq+X/36czr6DpRzMtfHDO+k4hGTKlbUDY4Sg/wP/Y2vn31Msg9jpTaRrUH/+lDeHto5C1uhjuK46nf9MvJY7hk7lScNhAFwerYvgzzk7KSpZRs92MxjbJrmJUpyeWft+QAojv+uu9QVvN2okL992D/41NcjPNiG8ArEwk8y//75um6qv/oXTZiSyWyUhZbMwfjoa95MxuHav52BZR0zSQ0RnrWa3YtaHVJWVnvC+94/uyMMTOlPlcGM6btj7zIebHiVcbdNaMi2B56Yfuq52flpnjYeAEDP9JyYy8e4elFvtzH9vBzarnRHXdT7FXlofY2wsHRcvovOK5Vi6ND87qMfj4LENz/LJ9tv5s3yOzm3aU5SXy9oduzhYWAzAkFET6bS/LT1Ch1FuKcH/9kTuTIhkWFI0NUhubX92k7qdT83pdZMOnDCpqJTyTbSukw2X5wJTjnueBtrcyco5dJIa/YFDZXz+yQ68Hgk1HiIqvMzLrmQfLvp7dYRMiKVzUmjd+kajkX63/J2VOX/ktrfXcO3BVfw9621m+M/DOv2/9O90M0Kv/er0DIsAKyzau5np/cbx2tb3kMLEK4PObvczu9vJoaLlRIQOJtZPG7JRWF3ML8PGktjPyN12JzFTfs3++6bDmiO4nDZ0ldXkvfJvTGGC0P9spGbzfDz71mDOmYtY9DCZjuGEGf0w+/kz6Krr2PDt16yb+yXj76pfdiEED0/owsMTuiClZOprK9hprWTm93v48x2Nz7dbU6V98Z2rQB8eG0B0UjAbfzpEYs8IgiP96NA3ioTu4RQcthEa40/AOZyQ/ELSmUxgMrVom5L9C4n+QAAuXLEGMsfv5Z2NWg+mACHp1qUzrnl5hJvbcLAincGP34E5LJDcb/NYEmnET6dnZFjrSWLWkEqB4CPqkkHVBnprqZ2vvtpDVbkD/4PVhAF2A3VfCJZsO93i/QAPOVm2Rvf59Y48DLh5KuevAOQlz2BI8pB664zuNowOCxfy70Kw7ltCYfFSusXdQLvAszuz0l82foTw2ugaHEeNqwaTXvDqrkVAMpMHjCM+RsuuGNhvGFXr51Gy/We8//oId6Uk8f+ewhDZFsOku2DSXVR/24//fqnlrbFJDx/94T56jr2MTgOHcmjrRk7WR1oIwX/uTGHUK8v4eFcuj9b0wmQ58c+oulyr0RsM52ZCCiEE427vytxXtjDnpU1c/Uh/wtoEYDDpiety9iaJaY08FRVUb9yE9LiR1dW4rcXUbNC6ybrb6zEegrY/VpA+0U3f7t244sZb8Nrd5D6n3XeJvnwIxhB/bIuz8NicLA82MTo8qFUlMWtIBXofcbSp/GjTzcfvpuGXaceCpNQoiOoTzqN39wVg+14r3324k7BsbRq0wH1VjPzPSjrGhbDtUAk1Dg8CqCp3EB9YSai7klUj/sGICb874X31BiP3mYt53JvEkQ1/wWBqy3sj/3jWj+9I3lf46yTXeD7inZVpvM4TeEQyE/yyGRJzbFh/1FW/ourdeeTd+QY6r4uYcZ3xn1w/CZb/VQ/Qfc1y0jKr8Xg8lORms2K2ltemTadTNwckRARwW3QE1sM2Zj+7jhv+Mgj/4Po1zMBwrS338PZiug0/NxODR7QNZMafBvDt61v45uXNjL45mc4pp58ozhdUrV1LziOP4ikpqbdcmM2E3XorMX/5M6vuv5/w5Sv4w+8fxj9Sq5Do/AyIaW1w/ZBD4Kpq0ncsQm/QczBIkG8WTIpsHXnnm6ICvY9IX62NxJzz3T7adwjFk11NhUkw8YE+9OsSUa/tvldyJL1eGs3k99YQbfPil1dD9mEbRzJt6AON+AcYkQISEoK5a3B7+BZCvDVNvne3uM4EbfsPek8p0lPKfT+M4eqOVzKj12OYDGd+86rSUcrBykJGRMRji5pMX+vb3MT/WCfG896ASfXW9UvqgnNSF1Y7HiKwIotbX7660X2Of24mPZ/rTk70lfR68C3S5v/IwS0bGXXLnY2u39AzDwxk34Z8Vnyxjx/eTqP32HZ0GRyDvrbW13tcPBt/OsTBNCuLP9nF2Fu6npMJwsNjA5jy297Mf3c7Cz7Q+olfqsFeSknWnXcBkPDxR+iDg9H5+6OPjEQXEFD3N5B4/fVULVtO+rPPMuSdY2ke4oZ1xjOoAzs+XkXZwXKWmXayMbErQoYxMqBlTUUXGxXofYQr0ADFDqwLc7GSix/Q4/qO9E8+sQnFWu3ksmU7yOviTx+nji9+N5j9n23i0dEdmdLx2A2nsqpy0udpI1plcNO10sf3zMds3wJAG6PksN3OC+lf8eneeVyROIZ7B72EQX/6bdU/7n4Xp4Rpna9nfJe7+O/aDC63/8Q1wXYCjNNPWP9Q8gxIh8qgBHZ+t4LetzSS+KyykBhLFTFjR4HJzMBpMxg4bUazy2Q06+kxMg6Tn4G18w6w5H+7Kcq0Meom7Qa0Xq/jxqcH8flzG9izNp+cfWXc9OxgjKaz0wsna2cxm+dnEpUYxJFdJVTVNhWt//4gHftf/HOYngvSdSxnkCkpCWObNo2ulzBuHGt79SJwxUqk2404Lp2E3qDH0z+IZdla//r8tonE2IpJW5FD9MSJmFp4b+Bi0XobnZR6jCOjeTPEjjM5EPPACK5+aiDjRzc+OcXU5bvIswgGewx8OaYbYRYjS+4aWi/IFxYfwfVmP0ZlfMGaTjfQNeXaRve1K+MXisq+0cogJO9O/JiVN67jqb43U+Vx8+6+hXyx7cw6Wv1yeD6hesGYTrcBcOfgNzCYYrCXLSEz68N6625a/As529vSrk8uSSF7WbkygOX//AiPvbL+Tte8CToDJJ5Zjr3OKTHc9vehJPWKYPfaPLJ2Fte9Fhzhxx0vDiM6KYiK4hr+9+c1VJY2fWXUEnvX55O7v4xti45QkntsoHl5ob3e80uJzmQicdan6AICOHzTzTj2729yXePwYRhcLvJXrT7htcWLFwMw4aobyDH5kWTNZ9OmTXz//ffnrOznmgr0PsLp8eIUcO8D/bn7131oF9/4xAhLs0s4bJakeAx8N6EnoebGa9qHNswmylnMrinvMezWmZgMjddkblh9LIfNP4c8QsfIFMzGAG7o8yRLbtpCpEHHZ/u+o6gy67SOq6jyCGnlxYyK6YK+NmWuTmdkcMochDCSkfECNpuWHazKVsyGuYLAqELG33oFE5+8nqTILHZkJrL67W+wHdhL6jMfUPa/h2DrLBjy27rJz8+E0AkGXdkBS6CRH97exg9vpZG5Qwv4Fn8j1z6eQki0HzWVLj55cg07Vuac8Xt2G972hGWWQCNJvSMJj7108wb6p6SQ+On/kG4XR+67H09F4/MtJE6dikenI/fRR8larQV7t9vN66+/TlVVFQkJCeypbb+/L0VL8Obnd2ZZVy8kFeh9hMtdO+T+JD0DHG4P92w7hHBL/tkvqemdSUmbXV+yI6Q73Qfd2PRqx41E/WTcP5nUpX77tl5n4MHed5DtcHLlvCtYceCz5h3Mcb7d9Q5eBFcn315vucXSll49/wVItmy9GZe7ii1LFyM9Jkbd0IOAoDCMoRFc8Y9f0b3dYbYfjOPTV3LIKOzA7DXTkZ0nw9lKtAZEJQRxy3ND6DkqjtwD5fz073R+/k86c1/dzH8fXUl57dy1AGu/yWDOS5uY/ew63K7TG9R0cGtRvefBkRbufGk4V/y2N7pW3DvkbLB0707866/jysnB9uOPALXpFI5d6UR16kTIG6+j83oou/c+ds+ezZYtW7DZtB5oLpeLVGs5nf3NGKxafnyzufV2V720fyN8yMwV2gQM+ibaZu0eL8NX7aLCX8/tISH0iqzfJ9jp8bApJ48al5vSyhISKw5S3PHyk75n3oZHeDmumnmdRtG/XeMzT83o9Uf+N/5VAN5Je7ulh0Vq1jJiTXr6x5+4/6ioCbSL/xUeTzWbNl7P3tXgH24lqXv9TI1D751Gu6BD9ZbZJn4E5rPbL9pg1DP65mTueGEYPUa0peiIVpvs2DeKIVd14JbnhhDbMQRnjYeCQzbKCqopzas+rfcqOGwjIMTE9D/0465XR3D9nwde8gH+eH59tFq4p6wMgNxHH2PvgBSyH/4DXoeWrqPdpEl0mDMHR1AQ9rfeZsCAAXTqpA0crJKwtqySSZEhdO6sDTzLzj47c/9eCOpmrI/wSkmvuJAmR8auKLGRXZum4C/96rfdHym3MWndTkpMfvjvzOJX+mKeAfR+J+9SVly6BpMFkvo/d9L1+sZPJtTwJLn2KjKsW+gU2bx0R+mH5rC3qorr2nbG466hMH8PbRLqb9uly9OUlm0gZ2cgDlskQ26oOeEzMIdHED/+Mjqb7Ozf4cB6pOKcDiayBBgZfXMyozlxZPDVj/YnY1MhlaUO1n57gK//uZER13em99hjeVtsVjt5GWWU5FXRpmMoST0jTshhFBkfSHlRNfHJl3af+aZ4KrV7MrqAQDyVldh++glTYiIV8+djGzmS0BnXABDSoQP2Af2JXLQYe0kJERERZGRkQL9BuKsh7MAeZm/SppC0Wk+cfa21UIHeR0hgSIfwJl+fFBVa9/jVbVn8beCxtul3d+ynxOTHTVSzzuPBL2cBAHHJ4076nu0S7qSw8FWOrP8d7cd8ftJ1b+96Nf9M/5rrf76dH6d/Q9uQU6dH+GqT1re9s2sHy368FV3oNgqPPEbv4fUHNPXt8xlbZ2m9JNoPGHLCfqzZlaz99mDd86FXd8Rwlnq/tJQQoi6zYvu+kaz5JoOVX+5n16o8nHY3eqOOsoLja/lZxHYKoceItnjcEo/bS/KQNpQX2bEEnJ9p/lojT6mWykIfEY63tp0+9PrrKXzlFdwlxfXWjZs4kV2HDrPgxRcRtSnSd/uFEOGsonD5irqEsJWVlWRlZbXKNOrqWs9HeKU8ZZ6bW83a5BTRfvUDxJaqGkIc1bw+ZijLLhtKR5cWaMTWH0+6v9Ce9xPs9KO4Ysspy3dzv2d4IeXXuKRg5aE5p1wfYG1JCbE1eiKNHnSh2wDIz/up7nW3y86q+b/j2/cfx+MMBGDm8wsoqayfryaodvBSYLiZ/pMS6DW28URk51totD+T7+lJn3HtcNhdxLQPJjTGn6FXd2Taw325563RjL2tKyV5VSz6eDdLZ+1hxRf7+O8jK8nZW0r3Rm7IKhpR2w2y9NNZZN39G4C6XjimhPpXtO2nTmXzwBTKgoIoLS3F5O/PsrJKUnTawEGz2YzRqP3NVFY26L3VSqgavY/wylNnrrTX3rAdEH2sR85n2/ewXWehr7sCIQRmg4Gr7nqHwpfWkLT1RaqG3ERATFKT+ww1deaIYRue6iL0/iefBHtS1/v46+b/surIIm7o+5eTrrt532oKLZXc4j+FAI8Xu34uALrQXSye82sGjv07m1Y9jjdwDdYd7wNQHJFJRHEi7z7/C9f9biid27Znww8H2fjTYQCGz+hMpwEXV2IqnV7HiOs7M+L6xhOQdR/els4DY6gsqaGqTGtb3rexgPDYAPqMa30TYJwvxvh4Qq65BtvPP2NO7kLAiBGUf/89ll69CBw5ot66aevW1T02GAx4u/aiwuNlckwwe4HOnTszbdo0jhw5Qvv2Z29uhfNJ1eh9hQTdKSL9T7W1kd7hWs3+jXnf82hhNcGOat4cfGyOUJ3BiGPIwwDkLP7PSfcZGNwdqRNUZKeesogmvYVhEe1YVVLEz7tn4va4cXocja771lfPILxw04hfM+SyFwgSN5Kc8DFuWwyEL2PthjHIoDW4SzrWbfPU3+4g5koPIaVtmPvqJpweJ/ZKbRDNxLt70KHv2c2/c74YTXrC2gQQ3zWc+K7hjLutG30nJJzW3AOXCiEEbV94nuStWwi96iqqVq0iYNgwEj/+CJ1//e6nvQZqk+ZE1NRw7bXXstRmx+Bx49yi5ccxm82YTCY6duyIrpmT0V9sWmeplRN4pTzl5FJRUjvdL23V+rSb49rh1en4S0IUHSPq39SLG307duFHTMYXuKobT3pWU7qLg8VfY3BL/MJ7Naucvx/4NAYBj294m36z+jF0dgorDsyqfyxeL/siiokttrDuvQ/R6Y0MGvs88Z1GMunq1XSMewMcsVQX+bMrLYia0tepTP4KnV7H+HEDcekcSJMHo86I3ebEP8RE55QY1SvlEiSEwPr++5jat6fdv99BFxBQ73UpJdXz5xNcXk6xxcLnX3zB4Yg2xJcWUpitzTx2tNdNa6Z+832E5NRNN/PH9SC00sXMShv9vk7l1VJt2HxpxYntjjqDCWvKo4R4y8hfNeuE1x22DFZvvRK3cNMv7s+YI/ucsE5jOkcP5fvpc/lDtyu4Nr4nTgkPrHqJtOxjUxgs3fYTlf4eOuQGkLtv97HMnGh/uEnJ0xh/5SLSlrVHZtReEdRoc4v+uGQZRq+ZoVO6kLWzhANpRSQPbnwovHJp8O8/AOehQxy+7Tas77+PbcECpLP2d3/WbHIf+xP9N28huLyckA5dqLL4c0+/Xjz22GM89dRTdO3a9RTvcPFTgd5HSClP2XQT4Wdiyaju3P3tF0SXFOOtDaCpew82un7cpN9TRQBsP/Hm6YbNWkqE7hF3Etzj7haVNTakC3cNepFnx3/OtQnaVAW3LX6cz9P+gdfr4Zv0L9B7BP2MXfF6PFiPZJ6wj9lfvYZ/iZeIq4eTeMsUrrpRy6yZvaaaGksFfbt3ZfP8wwQEm0iZktSi8im+pe2L/6TNX5/FYy2m6LXXyfn9Q+wfP56cRx6l8OWXCRg9itGLF/HgI4/gHjkWHTAtIRbQ2ux9gW8chaLdjG3GepYjmdyS+h3dQ+OYdvs1/PuV13DZq4DrT1hXZzBRFDmIWOtqPC4HeuOxvucdQ69md+n/0FnObPLpZ8Z8iP/K3/D54fW8sO1LXkn/AheCHs44Kg5rA1Sqy8vrbZNXlEXB9yvRI5gx7X6C/EMBqHJWE1ys1d5nPaXdYOs/KaHRfPHKpUMYDITdeCOhM2bgKijEefAApbM/o3rrFvz69SP2b39DGI2Y4uNJ3biHgSEBRJp863dG1eh9QHOnEQRIm6vdNO069TIC/SwEVVcQ5nE3ub6+wyjMOCnetaLe8qiOdwBQaV17usWu88iIf/PN1M+ZGtsVl9SOIXbPsdQA8d161Fv/45l/Re/V1nvlkVuwVZUBUGYvwyM8YPYw8oYu9Lssgb6Xtb4+z8q5oQXzOAJHjaLde+/SeckSEv/3CcYYbVxDlt3BzsqaVp97vjEq0PuAlkwM7li6hNywtvRL0QYslel0RJiaHiXqlzhA2y5vd73lhiAtgHocJ86x2hI7dv6eZct7oatYzVPDX2JisIsAnSS20EhwlPYH+M3zT9fbpveoCTgStX7zwSXgqNH6/ZtNJj7r/xz2G9PpPTaeYTM64RfYOtPKKuffgmKt08FkFeiVi9HRW5XiFI03O9dvJykvA9f4yQghcFdV4TAaCQxp+hfbnp0OgCG0/uAcIXToPeD2nF6uFtCuRIqKFiClm4wDL7Nu/WVMCXHx1yALeo+O9v1SCIqI4siu7TiqqyjLzwPgipE388Q/Z2O3eHEkBhIVoZVNL/RUmcop9ZSc7G0VpVHzi7QkZh38W2/ysqb4VkPUJepo082pulUf/Oxr2iNIuftmAJyVlSAEBeVlx/bl9VK47H3caV8SXLGfdtKGTQQTPWDaCfvzcxkpFtl43XZ0hmMpXKWU2s3hU/Q5FkIQHNyX8vJNGI0RdOjwMBHhI3FWGLHYfqTf5KlEJ7Vn4cx/8c6vb0Kn0zHjz3/DEhXOx/95Br8aHeH9+iCl5Jv93zB792z0Qs9dPe9q5ienKJoyl5u15ZX8tt3FNaDubFGB3gd4m9l0Y0zbTE5Me3omabNFWaK0kaxZBgMHVq6k48iR5Hz1J+L3vI8TIyWmOIKcNor7/JZg44lNILH6buw3prNu1Uh69P8QizmWLRse4OC6w5TsiaTP5UNxu12k/7wRSwjE9+rI5b9+Hb2+dnh66TrKyzcBEBExivg47QvIzw/G3K715OkxejzWI5mU5edxZEc6X734NE6vC71X4OwZxXXTH2RPyR6eW/sceqHnjTFv0DG04wllVZSTWVJSgUf6ZrMNqEDvEySnvhkrpaRNURY5wy6rW6bT6fjNlVcy6+uvmbVwIUM2b6arbq/24qP7aBMQhi3/IElt6k/OYTswh4P7XqDYXI7wSuyUsmnTsblZwzqZqMiMYePX62uX6NEbjOxdfBjr4Ru57ol3kFKStuJ9ZAAIHXRN/luj5dYbjIz71b0AlObl8Pd/3I5HJ8kcYODDG18n0BJMtNBGv17R4QrGJoxt0WenKADzreVEmQz0C/bNSVtUG70PsNrs/GR6klFLZ7Dpq5fIObwP6fXWW0cIgd3sj8NeP+VA3IAB3HvnnSTUONhUWkhiyQoqdMEY/UNBCIJjO9Z9gUhXDQeWXs/mQ3/CpiujgxjIqKGr6d/vM/Qcy3MzYMAX3PPWd/S8XLvhO/yO8fzmjXn0mdab4gNO3v3Nb5h5/91s+ayAbe93w5bRAb3+1H9gYbFx/O2N70j+9bUc0hUwcc5E5u6fy56SPQAcqThyJh+jcolyeL0sKbYxMSL4lGNRWitVo/cBERZJnK52UNGuF2DXC9jwJ9+USFVIZ4jqRkSfy/EKHboGXwAAocnJXP+nx1j+yv3gB15LG4QQSI8boT/2K3Jo6fUcNuwk2hVF8ogvMQUlAVC4fwlOVzFep5m+fT4lJkEbJTvpV68x4TY3+tp9TLjlBeI7f8eO5T/iqK4mf5fWP95Z1PymlkBTIL/t+1smJ03mxQ0v8uyaZ7mvj5a2uF2QSvKltNya0koqPV6f7FZ5lAr0PsCir61xT/gbGSFDKdu9FE/BLgLLM0gqXIq/9XvuKfmIJ6oNWP0bn/cyIDKSAeOHs7toDV5dLjvnd8Zh9BLljadd7+dwF+3gkH4HbdwJdJ+8FCEEXo+LFfPvxG1Zi8fuz4CU2UTH15/dSa+v/yvWddB0ug6azuFtW/hm1zN0TBnC1Icfb/ExdwjtwNvj3+Z3i3/Hu9veBcDlcbV4P4oy31qOn07HyLCzO+PYxUQFep9Q20av09G51yDoNajuFZfbw4705djm/x7w0GX1L2TuvJ7EHscm6HCV2Mhc9zJZ4nNkrAWjywt4iXCGU2DJoWCXdmPU32mg65hvEEKQsX0u+/e/hCnUire0F6MnvY9/4MnTFNe9n6OGrfN/ALTBUAbj6U2gYdab+efIf3L7L7cT6RfJLd1vOa39KJcuKSULim2MDQ/Cz4eT3qlA7wtkbXOMOPEX1WjQ06fvaGSaiXlDa7h6rST/tt+x+7qbmfjHB7Dv2MvGgtvxWLTBIoPbf0lg0oC6LjyOvPVkbn8GIfQkDn0bvV8Euza/T07JS+gseoK4jYHXPNusUblHLXz/HQ5u2QjAobTNpEy9+hRbNC3CL4Kfrvnp1CsqSiPSK+3kOVw80d53m21ABXrfcDTQNzJgqrq6mOe/v4kDeheMNHD5va+ydWYJnsxoPvjtPEZTQWzyPWSnaBN4Z+z7kO4xXTD5a5OTmGMH0yX2WK75XVvfI7f0ZVy2EEaM/Y7g8Ja3i2dsXEdc1x70GjeR2M6nnlJQUc6V+UXl6IAJEcGnXLc1891rlUtJXQ6EE0/nE99M43tHHqFeyVc3r2JBqhW3MQy3YzPB3vUERCVjdscyqPt8Qu2jKDamkrHmtUbfZl/6p+QWv4yjNJThY5of5O2VFWz55QfyM/ax9pvPcdXYEULQY/R4wtteHNP6KZemVGs5g0ICiPCxJGYN+fbRXSqaCPSuGhub3GX09up48bJ32Z9TgH+2AUfFh5iEhwFxv6bUWUjXP16J0WJiwBUfsTS1LxVOLa9NdvY6omN6YzL6s2v9bHLK/oarKpDhY+YREtH8mvyyT95n14ol9ZaFtok9s2NWlDOUaXewq6qGZzv6/ty7pwz0QggLsAIw164/R0r5rBCiD/AuEAgcBm6RUjY6FZEQQg9sAnKklFPPUtmVo+ra6Os33cxf808qdDru6XUf7RJG8NZ/vsBl0yYRSQjsh58hEONlERgtJrxeL7tXbKPSJaiUmSxe8jvgZ7bvCCYm9NcUlbyJ9JgYPOQLQqNalhHSaa+fD+fe/3yCX7BvXyorF78FVt9NYtZQc2r0DmCclLJSCGEEVgkhfgHeBh6VUi4XQtwFPAY83cQ+HgJ2A+qv+5yonwOhtLqYP/40Gof0EKHXMbD3bdiddpy7/dCFDIXydVj02pRqiaNT8Ljc/DDza9KK9hIb25VOnTcA2oxPJpON0uo3QBpJSfmciNhuLSpZcXYWGZvW1z03GE0Ehp9ZDntFORvmW8vp4m+hvQ8mMWvolIFeahmzjs41Z6z9L4FktJo+wEIglUYCvRAiHrgCeB7445kXWTlBg143W7a/wa0R9rqXV63sR5XTn7gOkwkNv5Iu2wdh1luo9lSSvnAjyzesokRW0Du8C1NueZhV63+Nn6Uvgwf/iSVLe6PXuwnQTyAytndj794km7WQnSuWgJTc9tJbpC/6hcTe/c7aYSvK6SpzuVlXXskDPprErKFmtdHXNr1sBjoB70gp1wshdgDTgO+A64CmGm3/D/gTcNLRCEKIe4B7ABIS1GQRLXJcr5uaws1Q9TkAOo8RR/R1lFXsI0psIrj3XCqWdGeVxU6iJ4pdpiMUrrcRogtg+tDJ9JkwCJ1Ox4Tx39TtOu9IX+KTNtGx+50tKlJFsZUPH74Xj0sbxOR2Oplw9wNn5XAV5UwtLrb5dBKzhpoV6KWUHqCvECIUmCeE6AncBbwlhHgG+B5wNtxOCDEVKJRSbhZCjDnFe8wEZgKkpKTIk62rNHBcjX719utACOIcbUmetByh0yGl5OdP/4E+5nPSDFqqhEP6QgKEhUm9xzBw6ggMxvq/Ch63h1VfLuBQVjLu8m6MHzegWUVxVFexYvZHmP0D6oI8wO5Vy2jbpfVPsqz4hvlWG9EmA319NIlZQy3qdSOlLBNCLAMmSylfBSYCCCG6oDXPNDQcmCaEmAJYgGAhxCwp5a1nVmylnooCjrS1kJf9NARpo0y7Xr6y7mUhBD0HX8nPS7XkX9dNuR4/nZ6EXh0wmI+NSvW4PUiv5OCG3axYuYJsRxFmaaKvswdSyuZNVZj6E+mL5tc9N5jMuJ0OopM6nGQrRTl/HF4vS0psXB0d5rNJzBpqTq+bKMBVG+T9gAnAS0KIaClloRBCBzyF1gOnHinlk8CTtfsZg3bzVgX5s6zGW86+TtrUeu08nek4/H8nrFNmLaEgvzMAXfslozfqT1jngxffIc+tzc5kxMCw9v2J3OckujSSvPU7aTuk50nLIaVk96plxHbpSp8JlxMSFUN8957UVFViCQg808NUlLNidWklVR4vkyIvnb4hzRkwFQssFUKkAxuBhVLKH4GbhBD7gD1ALvARgBCirRDi53NVYOVEMqoTAN27vUKXy+aj9z/xBpN3e2bd43/98w2yth2o9/qRbQfIc5fgJ01MHzSJRx97lIl3TCNiqHa/xOtsegLxozI2rqU4O4seo8bRY/R44rtrXwwqyCsXk1RrOf56305i1lBzet2kAyd0lZBSvgm82cjyXGBKI8uXActOp5DKycmT5LoB8HrdhOVFc1sbE1Udu/Dt6p/57vvv6LS5PTuO7GVQjwEs3bEaI3oefOhBAsKP1XQ8did6wHiKtkxHdTW/vPMGMR0603PsZSddV1EulOOTmFl8OIlZQ2pkrE+ozV7ZxAVa6a6tGBzBmLoY6Dh+IDu3bWdvVRbFWdsAWLpjNQCThk2oF+QB3MKNCT3SeWIee4Ds3Tv45Z03sBUVADDs+pvRG04vG6WinGvbKrQkZr6ce74xKtD7ANnEyFgAr9eLbX4Wwmym7ZDhANzw8B2sn7eMnQf2MOO26zmy6zD+oYF0GlR/MJSUErnTDgRiLy6rW+6ormb3yqVk7dzG4W1bcdXYie/eE4PRRFxyj3N1mIpyxlKtWhKz8eGXTvs8qEDvEyRaoG+sRp+/ej5GazTeMYWYAsIA0Bn1DL1+PEMZD0BYfON55PfNWkKAPRBrQAG9JlxVt3zH0oUs+9/7AEQldaDHqHEMuOKqRvehKBeT+ZdIErOGLq2j9VVNtNHbS/NxLK2BYCeJE6a3aJfb3/qesNwwbLoSevxpGvrj+tlHtNNu0A699maGXXfzmZVdUc6TTLuD3VU1/PUSSGLW0KVzN8KH2e1ajxpxXD56d2U1BTPXoa8JJeyqLugMzf9OX/vuLMJyw3B47HR68jKMZlO9100WbTrC8DiVYlhpPeqSmEVdWu3zoAK9T3C7qwCwWLSairummiPvpKIrD0Z3RSmh3bu3aH+VRcUAmPV+5G3YVe+1ktwcDm7ZAEBgaPiZFl1Rzpv51nKSAywk+fl+ErOGVKD3AVJqfdyNRi0rZO78+RhLI9FPqSR++IwW7++ypx8i8O6OANi25Bx7H6+X2X/+A+vnfQWA260m41Zah9LaJGaXSm6bhlSg9wFeqQVcnU5rnvFuM+IMzyVuxLTT3mdop7Y4PHZ08tivyOFtW3DaqzH5+XPlH58kSWWiVFqJo0nMJvn4lIFNUTdjfYCWcw60JKPgCSnDUByFx2lHb/I77f16pRshtXb/zPQ0vn3lH4REx3Dd088TEt3mzAuuKOfJfGs5MZdQErOGVI3eBxxtutHmhYGAoW3RuwIo2bbljPbrFV5klbbvXSsWozcYuPXFN1WQV1oVh9fL0pIKJkaGXDJJzBpSgd4HSK/WdCOEdoEW1F5LXlZjLTrtfXo9Xgw6E4Ya7cujrLCAqKQOKm+N0uocS2J2abbPgwr0PuFo083RNvrSNK0mb4lufCBUc9hzSzELP2qiHUgpqSorwS/o0mzfVFq3+bVJzEaEXrqVFBXofYC3runGgLumCvdaHc6wAiL6DW3xvqRH4jhYRtmsfQCUVOexY+lCygvy6ZQy+KyWW1HONa+ULLBeeknMGlI3Y32AlC5AIISe7G+/w2Bvh2W6EZ2uZb/YnioX1g+248rT+uUfsKWx9dBC5M5fEDodPUaPPwelV5RzZ1uFnXznpZfErCEV6H2AlB6EMFC6exv6tDicHbKJ73tTi/bhOFxO0XvpAASNa4cpIZg2HYcQvaE3pbnZxHTojGjhF4eiXGip1nL0AiZcot0qj1KB3gdIr0sL9Kk70ZtCibt5Yov3UbO3FCQEjY4nZGJS3fJuw0efxZIqyvl1NIlZuPHSDnWqiuYDHI4CTCWxmPLbQb9qzIERLdpeeiUVS48AYEq6tGs+iu/ItDvYU1VzyY6GPZ4K9D6gOGsN8Vv/iMdUQZsJLWtH9zo9VKzIBsDSNRxLsspfo/iGVGs5gAr0qKYbnxBTchN6VyCBd4dgDgpr1jaeKhfF/9uFM1PL6GeMCyT8pmSE7tIcUKL4nvlWG10DLCRegknMGlKB3geEZA/Hg5PQTr2btb4rv4qC/9P62geNT8CcGIy5YyhCr4K84htKXW7Wl1fyYELMhS7KRUEFeh/gKXMC4HV40Jn1J11XSknpvAxAa6oJuSzxnJdPUc63RUeTmEWqe06g2uh9QsjlSdoDT+MTeB/Pvq0IZ6YNU2IwEbe3LE+9orQWdUnMgi7NJGYNqUDvC5o54k96JeULtNmoIu/ortrjFZ9U49GSmE26hJOYNaQCvS+QtT9P8UttSz2Mp6SG4AkJ6PyN575cinIBrC6rpPoST2LWkAr0PqE20p8kztfsK6ViudaNMmh8wnkok6JcGKnWcgL0OkaEXbpJzBpSN2N9QV2NvvGXq7YUUPrVPhDQ5tEUhLqcVXyUV0pSreWMDQ/CrFJ21FGfhC84GuibiPRl3x4AIObh/hgiTn/GKUW52KVVVFPgdKtmmwZUoPcJTTfdeKtdSKcHU1IwxpiA81ssRTnPUq02lcSsESrQ+wB5NM43CPTeahd5L24EIGBw7HkulaKcf6nWcgaHBBJ2iScxa0gFel/QSK8bV34VuX9bh3R6CLk8Cf++pz/blKK0BseSmKnafEOn/NoTQliAFYC5dv05UspnhRB9gHeBQOAwcIuU0tZg23bA/4A2gBeYKaV886wegXKsSo8W4G2Ls7BvtwIQMDSWoNHtLlTJFOW8mV+bxEy1z5+oOdc3DmCclLJSCGEEVgkhfgHeBh6VUi4XQtwFPAY83WBbN/CIlHKLECII2CyEWCil3HU2D+KSVxvn7TuLKfl8T93imEcGYIxSIwOVS8N8azndVBKzRp0y0EspJVBZ+9RY+18CyWg1fYCFQCoNAr2UMg/Iq31cIYTYDcQBKtCfRcYYLZgfDfJR9/XGFBeIMJ48742i+IoSl5v1ZVX8PlElMWtMs+5YCCH0wGagE/COlHK9EGIHMA34DrgOOGn7gBAiCegHrG/i9XuAewASEtSAnpbw6xlJ7F8GU7W5AEO4BXOSunRVLi2Lim14Uc02TWnWzVgppUdK2ReIBwYJIXoCdwEPCCE2A0GAs6nthRCBwDfAww3b8Y97j5lSyhQpZUpUlLpx2FL6IBPBY9rh31t9dsqlJ9VaThuTkT5BapxIY1rU60ZKWQYsAyZLKfdIKSdKKQcAnwMHGtumtl3/G2C2lHLumRVXURSlvqNJzCZGBqskZk04ZaAXQkQJIUJrH/sBE4A9Qojo2mU64Cm0HjgNtxXAf4HdUsrXz2K5FUVRAFhVm8RMTRnYtObU6GOBpUKIdGAjsFBK+SNwkxBiH7AHyAU+AhBCtBVC/Fy77XDgNmCcECKt9v+Us34UiqJcso4mMRuukpg1qTm9btLRbqI2XP4mcEKfeCllLjCl9vEqTppTUVEU5fSpJGbNoz4ZRVFarTRbNYVOt2q2OQUV6BVFabVSi7UkZuNVErOTUoFeUZRWa761nCEqidkpqUCvKEqrdNjuYG9VjWq2aQYV6BVFaZXmF2lJzCaqbJWnpAK9oiitkkpi1nwq0CuK0uoUO91sKK9SzTbNpAK9oiitzuISlcSsJVSgVxSl1Um1lhNrVknMmksFekVRWpW6JGYRwQiVxKxZVKBXFKVVWVlaoZKYtZAK9IqitCqpVhuBeh3DVBKzZlOBXlGUVsMrJQuKyxkbHqySmLWA+qQURWk1jiUxU4OkWkIFekVRWo351nKVxOw0qECvKEqrMd9qY2hIIKEqiVmLqECvKEqrcKjawb7qGiZHqd42LaUCvaIorcJ8a20SM9Vs02Iq0CuK0iqkWsvpHmAhQSUxazEV6BVFuegdTWKmctucHhXoFUW56C0q1pKYqfb506MCvaIoF71UazltzUZ6B6okZqdDBXpFUS5q9qNJzCJDVBKz06QCvaIoF7WVpRXYvV41GvYMqECvKMpFLdVaTqBex9BQlcTsdKlAryjKRUtLYmZjXIRKYnYm1CenKMpFa6utmiKnW+WeP0Mq0CuKctGaby3HIGB8eNCFLkqrpgK9oigXrfnWcoaGBhKikpidERXoFUW5KB2sdrC/2qFGw54FKtArinJROprETAX6M3fKQC+EsAghNgghtgkhdgohnqtd3kcIsVYIsV0I8YMQotFOrkKIyUKIvUKIDCHEE2f7ABRF8U0LrOX0CLTQzmK60EVp9ZpTo3cA46SUfYC+wGQhxBDgA+AJKWUvYB7wWMMNhRB64B3gcqA7cJMQovtZKruiKD5KJTE7u055h0NKKYHK2qfG2v8SSAZW1C5fCKQCTzfYfBCQIaU8CCCE+AKYDuw645I34Yu8EhYXV5yr3SuKch5Uez1aEjMV6M+KZt3Krq2ZbwY6Ae9IKdcLIXYA04DvgOuAdo1sGgccOe55NjC4ife4B7gHICEhobnlr+fhxBi2V1af1raKolxcpkaZ6KWSmJ0VzQr0UkoP0FcIEQrME0L0BO4C3hJCPAN8Dzgb2bSxDESyifeYCcwESElJaXSdU3koKeZ0NlMURfFpLep1I6UsA5YBk6WUe6SUE6WUA4DPgQONbJJN/Zp+PJB7ekVVFEVRTkdzet1E1dbkEUL4AROAPUKI6NplOuAp4N1GNt8IdBZCtBdCmIAb0Wr/iqIoynnSnBp9LLBUCJGOFrgXSil/ROtBsw/Yg1ZL/whACNFWCPEzgJTSDTyIdqN2N/CVlHLn2T8MRVEUpSlC61RzcUlJSZGbNm260MVQFEVpNYQQm6WUKY29pkbGKoqi+DgV6BVFUXycCvSKoig+TgV6RVEUH3dR3owVQhQBmae5eSRgPYvFaQ3UMfu+S+14QR1zSyVKKaMae+GiDPRnQgixqak7z75KHbPvu9SOF9Qxn02q6UZRFMXHqUCvKIri43wx0M+80AW4ANQx+75L7XhBHfNZ43Nt9IqiKEp9vlijVxRFUY6jAr2iKIqPa5WBXgjRVwixTgiRJoTYJIQYdNxrT9ZORL5XCDGpie3DhRALhRD7a3+Gnb/St5wQ4svaY00TQhwWQqTVLjcJIT6qnaB9mxBiTBPb/1UIkXPcPqacx+KflrNwzK3qHMNJj9kohPik9ph3CyGebGJ7XzrPzT1mXzrPtxy3PE0I4RVC9G1k+5afZyllq/sPLAAur308BVhW+7g7sA0wA+3RJkPRN7L9y2gTmwM8Abx0oY+pBcf+GvBM7eMHgI9qH0ejTfeoa2SbvwKPXuiyn+djbrXnuJFjvhn4ovaxP3AYSPLx89zcY/aZ89xgeS/gYBPbtPg8t8oaPdp0hMG1j0M4NmvVdLRfDoeU8hCQgTZBeUPTgU9qH38CXHXuinr2CCEEcD3ajF6gfbEtBpBSFgJlgE8NMDmDY26V5xgaPWYJBAghDIAf2rSdtgtUvHPiDI7Zl87z8W5qYvlpaa2B/mHgFSHEEeBV4OhlXWOTkcc1sn2MlDIPoPZn9Lkr6lk1EiiQUu6vfb4NmC6EMAgh2gMDaHySdoAHhRDpQogPW8Pl7XFO95hb6zmGE495DlAF5AFZwKtSypImtvWV89zcY/al83y8Gzh5oG/Reb5oA70QYpEQYkcj/6cD9wN/kFK2A/4A/PfoZo3sqlX0Hz3F8R7V8Fv+Q7Qvs03A/wFrAHcju/8P0BHoi/aH89o5OIQWO8fHfFE6zWMeBHiAtmhNko8IITo0sntfOs/NPeaL0mke89FtBwPVUsodTey+5ef5QrdRnWa7VjnHxgAIwFb7+EngyePWSwWGNrL9XiC29nEssPdCH1MzjtkAFADxJ1lnDdD9FPtJAnZc6OM518fcGs9xU8cMvAPcdtzzD4Hrffk8N/eYfek8H/faG8Cfm7mfZp3ni7ZGfwq5wOjax+OAo5c+3wM3CiHMtZf1nYENjWz/PXBH7eM7gO/OYVnPlgnAHill9tEFQgh/IURA7ePLALeUclfDDYUQscc9vRpoqqZwsTntY6Z1nmNo5JjRmi7GCU0AMARtruZ6fOk808xjxrfOM0IIHXAd8EVTG57Web7Q32yn+W04Aq23xTZgPTDguNf+gtbbZi+1PXNql38ApNQ+jkC7obe/9mf4hT6mZhzzx8B9jXyb70WbeH0RWprSxo73U2A7kI72hxF7oY/nPBxzqzvHJznmQOBrYCewC3jsEjjPzT1mnznPtcvHAOsaWX5G51mlQFAURfFxrbXpRlEURWkmFegVRVF8nAr0iqIoPk4FekVRFB+nAr2iKIqPU4FeURTFx6lAryiK4uP+HzvgjqwpoASDAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#find all tracts and precincts centered in this clipPoly, flag for cutting\n",
    "# and, ID all nearby tracts and precincts to receive their pops and voters\n",
    "cutTractList = [-99999]\n",
    "cutPrecinctList = [-99999]\n",
    "tractReceivers = [-88888]\n",
    "precinctReceivers = [-88888]\n",
    "popToCut = 0.\n",
    "for t in range(nTracts):\n",
    "    x = tractGeom[t].centroid.x\n",
    "    y = tractGeom[t].centroid.y\n",
    "    if tractGeom[t].intersects(clipPoly) :\n",
    "        CP = Point(x,y)\n",
    "        if CP.intersects(clipPoly):\n",
    "            isSkippedTract[t] = 1\n",
    "            popToCut += tractPop[t]\n",
    "            if cutTractList == [-99999]:\n",
    "                cutTractList = [t]\n",
    "            else:\n",
    "                cutTractList.append(t)\n",
    "            x,y = tractGeom[t].exterior.xy\n",
    "            plt.plot(x,y)\n",
    "    else:\n",
    "        if tractGeom[t].distance(clipPoly) < 0.1:\n",
    "            if tractReceivers == [-88888]:\n",
    "                tractReceivers = [t]\n",
    "            else:\n",
    "                tractReceivers.append(t)\n",
    "xp,yp = clipPoly.exterior.xy\n",
    "plt.plot(xp,yp)\n",
    "\n",
    "for p in range(nPrecincts):\n",
    "    x = vtdGeom[p].centroid.x\n",
    "    y = vtdGeom[p].centroid.y\n",
    "    if vtdGeom[p].intersects(clipPoly) :\n",
    "        CP = Point(x,y)\n",
    "        if CP.intersects(clipPoly):\n",
    "            isSkippedPrecinct[p] = 1\n",
    "            if cutPrecinctList == [-99999]:\n",
    "                cutPrecinctList = [p]\n",
    "            else:\n",
    "                cutPrecinctList.append(p)\n",
    "    else:\n",
    "        if vtdGeom[p].distance(clipPoly) < 0.1 :\n",
    "            if precinctReceivers == [-88888]:\n",
    "                precinctReceivers = [p]\n",
    "            else:\n",
    "                precinctReceivers.append(p)\n",
    "print(\"I have flagged \",len(cutTractList),\"tracts w pop\",popToCut,\" to reassign to tracts...\")\n",
    "print(tractReceivers)\n",
    "print(\"I have flagged \",len(cutPrecinctList),\"precincts to reassign to precincts...\")\n",
    "print(precinctReceivers)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "d7009c9a-d5ff-4570-855d-1fb439afc32c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#clipPoly block 2 of 4\n",
    "#visualize the to-be-cut precincts, just to confirm there are really xx precincts up there\n",
    "for p in range(nPrecincts):\n",
    "    if isSkippedPrecinct[p] ==1:\n",
    "        if notPolyVTD[p]==1:\n",
    "            for geom in vtdGeom[p].geoms:\n",
    "                x,y = geom.exterior.xy\n",
    "                plt.plot(x,y)\n",
    "        else:\n",
    "            x,y = vtdGeom[p].exterior.xy\n",
    "            plt.plot(x,y)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "0218d8e6-7832-479f-b44f-b37c117ee40c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#clipPoly block 3 of 4\n",
    "#now visualize the receivers\n",
    "for pp in range(len(precinctReceivers)):\n",
    "    p = precinctReceivers[pp]\n",
    "    if notPolyVTD[p]==1:\n",
    "        for geom in vtdGeom[p].geoms:\n",
    "            x,y = geom.exterior.xy\n",
    "            plt.plot(x,y)\n",
    "    else:\n",
    "        x,y = vtdGeom[p].exterior.xy\n",
    "        plt.plot(x,y)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "5d698e51-7b2d-40b8-b932-d22f20f09bb0",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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Q7+3gWlzNLHv5SwZVZLAxbTen3nIhdsuRfeNXvnsJY7bNB6DMYMRrsCCFggGJ2e/FHPBhkGrIvVRRcOr0uPUmvAYzLnMU3ohE9DF9sMYPICY5l4Re49Afag9C1Wb46KaQiWfKAyF34rhs0LXNBOb0O1lStoQFJQtYXLqYZl8zBsXAuJRxTE2fyuRek0my/bSZLqgG2Vy3meUVy9lUt4mCxgKKW4pRpUqMKYarB17NTUNvwqC03QR3JDQTkMYJidvh482HluH3/rSBSqcEUKWClKHRZoS5haSYJgJeP6UNKQSkGYUAyVHVpGebSR8/jITB/VCUnulJ5Nu4Dk/+TvxVLvzNJgKBBA43uVeUJmx9HNjOHI8+I3PvcdUfoPqf3+AuqUOxxKJTjt3dNWh2IHMcKGYDOrOZqm1eEqti2Rq5m363TKR3Yp8j1rFu8V8IVqzG0LAb1duMUIP4kASMNoQ5Cp3RjioUlIAXxduC3tuC0e8iyusiwefZ7xfwCMFus43qyCT8UWnYzTHYmyuxtVTTq74Il97Eq/0nUJqQTaQxkihTFNGmaKLN0USbovd+N+lMOHwOqt3VbKvfRl5lHisqVuBX/USZopiUNompGVOZkDoBm+HgAca2+m3cMvcWGr2NAGRGZpIdnU3f6L6MShrF6OTR7drx70FTABonHLWlLSz7tIDiTfXYY0xMvWYA9RVOXM0+FEUQmWAhLSeGqISfPCcCLieVy5ZSsqaIkhIjNd6Qh4lJcdIrvpb4BBVbXATWuBhs8ZFYYqOwxMai2GPaPCLsSKQq8RfsxrdxK/7iBnz1OvzeRECHTqnGYHdiSDBhSLIhDAHwB5GBABIdIioRY7/e6HqlIw4wxXhLqqn+x1KEKY6Aqxycjfi2fUPQXYeaYcU0aSQxky9AMVlR3T5UTwDV40fqVFBVgn4PQb8X1e9F3WRA744FVBTVdFAbiuyVnPrw5R36O6lBP3U1m6mrWIOzegOiajMR9UUktdQQGfTvva7YYGBZZCxfJ/fFa4nE5XfR5G2iydeEeqjYSPuQEZHBlPQpTEmfwojEEeiVQyteh8/BlV9fSVFzEQ+Pe5jpvacTZ2k1h1a7oykAjR5NyZZ61i8oRaqStP4xxPeys2tNDRsXlwHQd0QCgyen0SsnBtHWkbyUuHdvpXRpPiXbWyipTcQRaH1hzyDcGBU3RsWHQR/AaAhiMkmiolVie8WQlDuQqJwBB3Wsx4va0oJv3Qa828vwVQTwOeKRMmTKUGjBYKnGlGbAft6pKEkZx7wfovSBd4FeAJgym4m57jQaln9Fw9LZeBeuRV8aQDWBMrUPaT9/hMiBpxyxTilV/N4mfM111C9dDysjUIIh2RP/OBaj6WDl0BlIrwOHtwmzJRaDoXW3SlWqtPhaaPI20ehtpNHbSJO3CU/QQ4QxglhTLP1i+hFjPvxCsDvg5rvd3zF391yWli3Fp/pIs6fxzrnv7HX57Aw0BaDRY6ne3cyHfwk9d71JRyBs7jFa9Pjcgf2unXxVf4ZMOnjDUlsJeP24yktxVlTirG/B3eTB3eLD5/bh8wTxeSV+n8TnU/D4DDT7YlHDxgaL0oTBVEBGrouEnNHYbHaE3oCiN6LoDKEZhKIPhY2QKkIGkcEg/oAfny+A1+/H73QR+6OK1dCA6tDj9yURStsRxKAvxxjjwJRuxzioL7qcYQjjsfmF70vd/+biWh9AGEO2efsEhegLJu49r6pBapZ+QN07r8HiEkQAmJROr9/8mYjstu/BeOmr29m9QyEhAu655bnjlrs74/Q7eW/re7yx+Q3qPfUkWhOZ0XsGMzJnkJuQu3ejV2eheQFp9Gh0eoVgQN3b+esMCvG97FTsbGTPGEZnUEjNjj6u++hNBiL79CGyz5Ft1ABBf4DGbVupXLuFDeu81LWMZEdeCy1rNhKtKydaX06KYQtxhrYlA/Gpfaj2PYcXOwZLGZFZRRizkzHmDkOJm3IcLWud+vcX4t5qQRjBkFhLwu3noZj2N3Upio6kU68k6dQrcZZvpvj5h+DjrZQuvhbdNWPIefiNw94jqAb588Ib+KBuLWOy4nj47C/bvR3dBSklH27/kGfXPEuTt4mJqRO5eejNjEoa1emdfls4ogIQQpiBxYApfP1HUspHhRC5hNJA2oEi4GopZXMr5aOBV4AhgARuklIuE0L8AbgVqAlf+qCU8psDy2uc3Cx6dxsbF5URk2zF4/TjdQdQA5KgX+Xsnw9F0Qu8rgCKTmCNNB7kXtfR6Ax64oYMIW7IEAZdLflmzi42LNpFffNAzN4R6NAREH7Gn1WIIgMINQBCQQoFKXRIoaDT6TEaDBgMevQGM5WrVfqUK7jdqWwtV3ArNpIiVAZFBDEZj30z0IHUvz0X5xoFodcRdU4kEZNOO+z1PlcDzduWoYuMYM+Se/CtlaGEsIeguqWQ+767njXNDUyN78XTZ36K8QSNCNrkbeLRpY8yv3g+45LHcffIuxma0L3dko9oAhKh/1E2KaVDCGEAfgTuBp4D7pNSLhJC3AT0kVI+0kr514EfpJSvCCGMgFVK2RhWAA4p5dNtFVYzAZ18vPdYHnVlzlbPpZ6TTmycmfhYC/FxFmJizBgN7ddBHi/BQJB/37kQp03Hg09POaqy+T8WU7W2iuhKDxlhK1cDkiqrgrQZwGZAH2XCEmMiIs5KTJKV+CQ7RtORJ/Xe3VXU/Pt70PVCemuJv3U4OruBxq9W4C/1YkiJxH5qf/R946hZ8C4NPyxA7qxCt7MaEQgp2GCyAWLNpDz4e+JGn3dw29UA/8v/PS9t/RKvKpmVM51fjPtHm/cB9ETumH8HS8qXcM/Ie7h20LXdasR/zCYgGdIQjvBXQ/glgf6EZgYA84A5wH4KQAgRCUwCbgjX5QN8x9QCjZMKX0Dlo/xSPjN4GKSo6BWBIgTGgMQiQ51Q+TcllO9TRiLxCPDpBapBIMw6DDY9cZmRzJjWm/SUo4/Nfjx8PbcQvRSk5B69p8eoUzPg1AwA6qsdbF9ejmd7A5YmHxG1XqJqvOj4STGqQHX48+Z+Ecy4eXir9Zbe8wSYJ4EutOCL0FH7yhbQWxFKAjLgxVdmov79CqACKQcTrR8KA4AB4DFsJ+0XU7CkHbyDdQ/5JV/z+LI/sNPtIcdq4dEJjzMsbcZR/wY9BSklyyqWsah0EdcNuo7rB1/f1SK1mTatAQghdEA+kA08L6XME0JsBC4APgcuB1qL2JRFyMTzWthklA/cLaXc85d7hxDiOmAV8GspZcOBFQghZgGzADIyMo6mbRo9lLxddTz02UZ2VjsYkBzBmF8M5txhKXvPO1x+KqqdVNe6qKtz09ToxdXiw+cMoLgC6Fx+cAdRHAEMDX48JR4++6EKh11Hv4kpXHxBP3S6jh+dbVpaQSRQsa3xuOqJTbQz/oL9s4X5/EHqql3UVzloqXPhqvdAYTM5jQFUT6DVeqSq4qsUGDND31V3JULxI/Qq+jgftlP6Y5vQn+I/f4HeFVJaPmMJJn/vvXWY/Tk0Fe8mIFVM0QkY7dF7z9U5S/jrD3cwp6oAmyK4b/CFXDvyjyjHEcOmO+MNevmi4Ave3fouOxp2EGuO5dysc7tarKPiqLyAwvb8T4E7gQDwLKGE718Ad0kp4w64fjSwHJgYVhrPAM1SykeEEElALaHZxGNAipTypsPdXzMBnfi8+mMhj329mbRoC3+6cDBT+ycel11fVSWr11Sx5IcSfDtasAbBrQOlt5Uxk9KZODal3c0Sfn+Ql/63HvJD4xl/jp17fjW2Xe/RGvMfX0KqI0jy/aOJiWs9eJq3pARDYiJKG10wVVWleul8XFuqMexKRcifxowBcyMpD01j6e6PmbvrC+ZXbcOjSs5Kzua3pz5HnK2bRfFsR2rdtdz1/V1sqN3AgNgBXDXgKs7uczbmbrq+0W5uoEKIRwHnvrZ7IUQO8JaUcuwB1yYDy6WUmeHvpwEPSCnPPeC6TOArKeVhg11rCuDExekNMPrx73D7gyRFmvj75cMxGxQkoU5claGptgRUuc93uf93X1DFH1Q5b1gqhgNG+R5vgHc/2orrh+r9jjcnGrjjN+OIsBsPK6PLF8Bq/KkDDAZVnE4/Lqcfp9NPQ5OHpmYv6z4owCQFjkgd19wzkrTUjjc9bVlTQcT7O2lB0mBSUAAE7FGdHr3AbVAImnUImwGd3YgpwoDNakQqPgL6WowiiN/jJegNEPT4CfhDLzWgQlCi+KFXZTRR7lB7Xk17i48jl6Ai0CEZE5PAL0bcz6j0czq8vV2FlJJ5u+fx1MqnaPI28cSpT3BG7zM63fngaDnmNQAhRALgDy/cWoDpwJNCiEQpZbUQQiHkB/CfA8tKKSuFECVCiP5Sym3A6cDmcL0pUsqK8KUXAxuPuXUaPZ7Hv96C2x/yLalq9nLNf/OOq77kSAun9N3f9m426bFZ9ByYtTey2s/L9//ATX+dSHRU6yM4ty/Ir+77nqE+PX5CgyYDrf+nN4WPj72gT6d0/gDWCBPbrAqoKioSlVDeEgkIKbF6IMkVJLLBjwnvYWoyhV/7ExAB/CJIQASpMzRSZC2iKGM7F9mHMixxNFOzriTWduImf98T5O1fq//F+pr19IvpxzNTn+mUgG0dSVvWAFKA18PrAArwgZTyKyHE3UKI28PXfAK8BiCESAVekVLuGQbcCbwd9gDaBewJO/iUEGI4ob/RIuDn7dAejR7K4xcNwW7ScUrfOIQILfgKQBECRQBiz+fQdyHYe53bF+Q3H6+jpN5NnM3IvWfkMD6r9V2Wl12Uw9dRJirKWqivchFREFIHQoJef2hTkEEn2BMYwIDAkWxExJjRmxR0Jh16kw6bzUBUpAm71UCkzcDwIYnt+yMdht7ZsfT+/cTDXuP0Oflm+1Lmb19OcdU2zDShl3qEO5trhpxBVlIcRosZs8WC2WrFarJhMVsReuWgEW4ucCF3dmCLup7dzbuZWzSXVVWrWFu9FlfARbwlnj+c8gcuzL7wsGEfegraTmCNHk1JvYtb31jF9qoWHjxnIDef2ueI03Gn08c7b2+mcW0dZhW8fe3c/PNcYiKPbL9995OtVM0tw6eDidcNZOK47jvqdfvdzC3I45udP7KxfjXNagEIFSl1GAK9yY0bza/GX8uwlMyuFrXbsKZ6DR9u+5BVVauocIYMFNnR2XsDtU1InUCk8djSP3YlWigIjROOvF113Pb2agJBleeuGsnknMPno21q9PDhu1to3tCASYXGSIUzfzaA0SOTj+q+y/MrWPTaFiwBiT43hmkz+jCg7+FjwnQWNc4W/rH0XZZWfk99cCuIIFIqKL50MqzDmJg2jksHnUq/xNhub7fubPxBPyPfGgnAGb3PYFTSKKZnTN8vlHNPRVMAGicM/qDKG8t289fZW0iPtfLKdaPJSrAf8nqvL8D//rse9/oGDFJQHakw9pxMzpnc+5g7wYoaJ/99ehUxTaF1i2YD6NOsjDk1jckTe3V651pQV8VDC55lU/M80LnBn0iacSSnpI7nkkGnMiTl+LypuguqVFlbvZbV1aspaiqiydtElasKb9CLIhR0QofNYCPOEkeCJYEBsQPITcild2TvVlMqSinZUr+Fz3d+zjeF39DobaR/TH8+PP/DE+L32oOmADR6PKoqmbeliidnb2VXrZNpAxL55xXDibIcOkxzc7OXfz/6I1FuQW20wsQLszl9fFq7/OeWUrJxSx3LlpRSu7MJW1MAPQKHTWHIGRlMm5aBxdjxduLHFrzNB0X/RAofCcoYfj78ei4ffGqn7HXoTHY27OT+xfezs3EnAInWRGLNscRb4rHoLahSJagGcQac1LprqXZV4/SHthwZFSN9ovqQbEvGqrfiDXpp9DZS1FxEvaceo2JkWsY0Lsy+kPEp408I+/6+aMHgNHosJR4fr2ws5fWPtgDQJ97Gf68fzbQBhx/V+n1evn7p70S5xwMwKXI3g0wxwLFHC90XIQRDB8UzdFA8AI3NXt77aAsyv46iz4r4z+eFOO06ItLt9B8Sz4SxKUTa2zcE8r9+/Ib3dz+FVfbhyUmPMbVv9449c6y0+Fq4d+G9lLaU8tjEx5jca/IRQzGrUqWouYj1NespaCxgZ+NOql3VuAIuDIqBaFM0k3pNYljCMGb0nkGUKaqTWtN90GYAGt2Oaq+f5U1Oljc6WNboYIvTg1Llxri2HgDFoDBsUAIfzByJMTzKVYNBqmurqS3ajKdsPbJmB0mVC8iQ5SxVLsAlT6WgMp2ANBNnqWLYOCsDLj0bxXB43/9jwen2M3tOIbs31+GrchPhlQgEQSQOq4I5xUrWwFgGD00gMy0Cg/7Ydsq+t24Jj+ffg0nEMueK94i3do91iPbGr/q58/s7ySvP46UZLzEmeUxXi9Tj0ExAGt2WEo+P5Y2O8MtJgTvkp27VKYyJtHFajJ1zE6KJEIKnVxQxe30FTWUOJmd7uDcbhi88eAO5RxooNvTBdcqvyZ06E6EoeGur2fHlXDatk9R60ogyVJMU58AWoWCLNmOLtWFLjMOWlIA1tRd6a/vkDK6pd7FsRQUFm+twlTmxO1X04b0CHiFxGAXSrscUayQ1PZIBObHkDojHbA5N0F1uD/PmL6F0506aa2twOVpwBFZhCAi8RpXImESi4xIwWCwYTWZMJgs2awQD+o1mSM7YHhOALaAGqHZVU+GsCL0cFZQ7y9lUu4kt9Vv4wyl/4NKcS7tazB6JpgA0ugVBKdnp8rKiKdTZL290UOYNpeiL0usYF2VjfLSd8dE2htqtGFrJ7lVaupm1b/ya83xL9zu+xTwCOfZWojKHk5jRH4O+dQunDAYp+OY7Nv1YQZPLhtMficrB6wgmxYFN34xd38i4K8eSOKZ9wjk4nH7yVldQsrORxmoXvgYfOkcA8z4hfIJIHBYFva6UQPkXmIJuADyKiaDRgttUhV8nsQYMmD0Ck6/1Tt5nUAmk2IjNzmLg0PGMGzEdWxuSsncEftUf6uAdoQ6+zFFGUXPR3o6+2lV9UBrGWHMsqbZUZvafycX9Lu4SuU8ENAWg0am0BIIUuLzscnvZ6fJQ4PJS4Ap99qihv7kEo57xUaHO/pRoOwNsZpQjLM4um/cMI5c+RkDoWdTnGpJSxyICPoLuRnLPvgWj6ehjsUhVxVtfh7OiAmdVLc7aJpz1Tsp3+yhpykQQ5LxLBRlnTD+m36KteF1+tu5sYPO2OsoL6tFt+Z6gJ4+AMYH+M6/mlPGjSIyPbnXdIxjw43Y5cbqbaXE1Ud9czebNKyjfvoVgST3WlvB1isQdp8eWmUrGoGFk5wwnO2Ugkbbo45c/6KWgsYAKZwWVzkoqnZV7R/OVjkpq3DVI9u9vUmwppNnTSLWnkmxLJtWWSoothRR7Csm2ZCz64894pqEpAI1O4Nr1u5hXd1BOoP2YGhvBOQlRTIi2k2Uxtdkbx+/3ser9Ozhl5/usST6N1MtfICmuY4KNeZo9rP9gLuvyFRQR5KwrokmbPLlD7tUaxRt38vnTf8XnriQmbTRX/fE3mCNaD+7WVsqri8hbNY9dW9bgLCrHXONHFw6rHVAkclgyGYOGkTvsNAb0zm3VZfKwMjcXc9t3t1Hc8lPmM5PORLItmWRbMim2lP3ek23JJFuTsRqOr10abUNTABodzvSV29jocB/xOgVINxvJtpoZaDfT32bGrlMQhMI86ITArAgsioJFp2Cq3YLho5tIdxSyZPAtjL/kSXS69ndgc1TWsvaDhWzaYiMgTWRGbee0284iMjOz3e91KBa8/gWrZ7+GEAZGnXcjk646q0P80Z3uFtZv+JGiXZspWLMC427HXoXgMan4Ey1E9U4js/8wRg2bQkZy9iHr2lCzgdvn345E8psxvyErOosUWwoxppgType+J6MpAI0uwx1Uqfb5qfL6KfH42OnyUuD2ssPpYafLi/9wf4NSUrl4yk/f7ckQ3w/isinzxuEzJxLZZyiRWcMwWA69GexwNGzfwZqPl7NtdxISQb+4rYw8qw9xE8+GTopl31Rdz8d/+RcN5asx2dO57KGHSc5qH3fVtuD1uFm3eQmbN+VRVbADf3kd5iYVEV6sdllVSI4gNjOTvgNHMCZ3KnFRSfxQ+gO/Wfwbok3R/OeM/9A7svcR7qTRFWgKQKNb4lclRW4vXlVFBVQZWih2qyoeVeIJqmQveYIUfwNRyf2hvhBZs4MfNjSzsmr/0A8mXRBFhP+eJSAgNtJARk4/+k65gMTcSYg9HjFSUr1iGau/2kpBTQY6/AzqVcTwi0YTOaTz3AzVoMqit75izbdvIVUPGcNmcNH9szAY29899Whpbq4nf8MidmzJp6ZwF7KiGUs4lZNE4oqADRkNyOGp/Pv0f5NsO7qQGhqdh6YANE4IpKry9XNPs23pYoZNmcrAwb1pLt5Gc2UpzuYWQn/PCiggAwFqquqoaDEAArsxQN+sJAzRI6gtiaDS1QejcDEkp5bcy6di7dV5o1dHYzNL3v+a7cvm43NXYrSmcsasuxhwymFTYnQ5VbWl/PjdxxR9Og+A4Lhe3HnXP7XF2m6OthNY44SgbNtmti1dzPhLr2TC5Ve1ycbsKlrPrgUfUrB2HRu31RKU3wFGhC4aj/SwNM/DshVfougsKHozeoMVvdGC3mTFYLZiNNswWW3YY6PIGNyX7FGDUI5x81blrhrmv/YulTsWgfSiNyYxZNrVTL/5cnSHcFvtTkSaIqldtBpzRCQzZt1Bv7ETulokjeOg+//FaZy0BAN+CvJXsHv9GtwtzXgcDupKizGYLYw5/+I2LzBaM4cx5MZhDAF8jTUs+2AOhdsK8XmaMFrt6PUWgkEVv8dJwOcm4HPjbqlGbXCjqh6QPznor5sDQrEQEdeXtAFDGHjqGHoP63fEzVbVRbXMffltqgoWg/QSlTSYsRfNZOiUkYhW9jp0R6SUzHvp37iamrjq8adJyjr0wrBGz6AtGcHMwGJCaYL0wEdSykfDSd7/A9gJJXS5Wkp5kA9gOI/wK8AQQpbZm6SUy4QQscD7QGa4/MzWksJrnHw011Szdu7XbFz4He7mJkw2G7boWCwREaTmDGT4meditByb+6AxOoHJs67haJw6Az4f7pYW6srqKFi1geKNG2is3M6WHzay5Yf3EDobUYn9SB80lEGTxpGak7FXIdSW1DLnpXf2jvhj0oYy/ebryRg84Jjk70o2LpzHjhVLmXT1jVrnf4JwxDUAERpm2aSUDiGEAfgRuBt4DrhPSrlICHET0EdK+Ugr5V8HfpBSvhLOCmYNp5d8CqiXUv5VCPEAECOl/O3hZNHWAE5sqgoL2L78R1Z//TnBYIC+o8Yy9PQzycwdidJJ3jhtRVVVyrYWsWnxSkq3rKe5eidSDa2QKrpILFFp6A1Gmqq3hjr+lCGcfvP19B46sIslPzaCAT+v3vMLTBYL1z757E+L6Ro9gmNeA5AhDeEIfzWEXxLoT2hmADAPmAPspwCEEJHAJOCGcF0+wBc+fSEwJfz5dWAhcFgFoHFi8tRLL7KkxYNODaILBtANHM3AobnUxcWx3WDFVV5F38Q4LMb2jaR5PCiKQvqgLNIHZQFXoKoqhet2sHXJSip2bMLZUIU74CY2bRBTr7uKzNye2fHvoWpXAc01VZz5i7u1zv8Eok1rAOF8wPlANvC8lDJPCLERuAD4HLgcaG1bZhZQA7wWNhnlA3dLKZ1A0p6k8FLKCiFEqwlUhRCzgFkAGRkZR9M2jW5MicfHq6U1zK9rYXu/cQhVRe7TsSyE0FChxg811YhtFcS6WkgLeMgyKIyKjmDagBz6xscd4g6di6Io9B3Rn74j+ne1KB1CxY5tAPQeNqKLJdFoT47KDTRsz/+UUKL3APAsEAd8AdwlpYw74PrRwHJgYlhpPAM0SykfEUI0Simj97m2QUp52Hi2mgnoxMCrqoxetplaX4BTY+ycEWXh2vQkjIqCy+vB6fbQ4PZQ3uKgormZCoeL3W4vhb4gpYqRGlskangncKyrhZHSy7TkeM4f2J8Em+aO2N5U7NzGp0/+CbM9ghv/8YK2u7cH0i5uoGHb/ULgLCnl08CMcOU5wLmtFCkFSqWUeeHvHwEPhD9XCSFSwqP/FKD6aGTR6Jl4VZVbNxZR4wvw0uBMLkiM3u98hMVKhMVKMnAoo4nT62XJzl0sKipmhcvP4sh4vmuBB/O20tvRwFiT4KzevTi9XxZmXfdaO+hp1BYX8fGff4/ZHsFF9z+sdf4nGG3xAkoA/OHO3wJMB54UQiRKKauFEArwMCGPoP2QUlYKIUqEEP2llNuA04HN4dNfANcDfw2/f94+TdLojkgp+aSqgUd2llHvD/LH7NSDOv+2YjOZmDF4IDMGh1SEy+1i3obNzC2rZJWq4yNbIh9WODGUrKa/u4lTIy2cl9OXUSlJWgd2FFTu3M5nf3sMg9HEzEf+TGRCq1ZajR5MW7yAhhFapNURiuP1gZTyT0KIu4Hbw5d9AvxOSimFEKnAK1LKc8LlhxNyAzUCu4AbpZQNQog44AMgAygGLpdS1h9OFs0E1PNwBVV+v6OMvCYHO1xeRkRYub9PMtPiIjvsnuXV1Xy9aQsLa5tYZ7BRGxWyTNo9LnKDbqbERXLBwBx6R598KQDbirOxgdfvux2d0chlD/6JuF7a+ltPRgsFodElXL52Jz80ODArgj9lp3F1ahy6ThyFS1VlfUEBX+/YxZIWL1si4nBZQpm+khyNjBR+Tk+O5+z+2cRFdE2ilO6G3+Phw8ceorZkNzMf/QvJfft1tUgax4kWCkKj0whKyUaHm4V1LfzQ4KC/zcyCMf2PmOylIxCKQm6/fuT2C3ViPq+XH7ZsZU5xOXk+ybyoBGa3CH6Tt53klnqyVR/ZVhPTouycNiIXk+XkWlTetXolC/73Ek3VVZx372+1zv8ER5sBaLQrr5bW8LfCShoCQQBi9DoWjB1AsunglIvdgWangzlbtrOwuoFl0kC5NWSaSmms5cI1izGhEhcZSe/M3gzIHUF63+wek2P3aAj4/fzw9musnv0Fcb0ymHrDLHoPHd7VYmm0E9oMQKPD2eJw8+COMgBeGNSbRKOeEZE2rLru22FG2uycN2wI+UtXUxPQI6Qk2+fiF4oTb1w0tQ2NlDc7KN+4hWUbt6AEAyRE2Bk8dAjjp00/phSU3ZEf33uD1bO/IHvMKZx792/QG7qnwtZoXzQFoNFurG8JZQO7MS2ei5MOu6Wj2/BDUQl3bt5NpcXOgICDJwf1YVzG/olY/H4f29evY/umjewuLqbK4aQqbxULly4nMyWJM86/kJT0nr1Impk7kvyvPkVvNGqd/0mEpgA02oUdTg9fVDcCsLD+8HmBuwtrKmu4Zkcler2BP8WauHVYbqtuogaDkcGjxjB4VChRjMftJm/Bd6xauYpdVbW8+Mp/SbCamHH2OfQbNryTW9E+BLxeAPqNPaWLJdHoTLQ1AI3jYrfby11bislrcmJRBOckRHNHRiID7d178bTJ7eG0Rfk0KwY+H9qb3JSkY6qnaOtm5n75BeUtLhCCSEUyZepURpw2pUftOfjoiUeoKyvh1uf+i6Jtnjvh0NYANNoVKSUNgSD/La0lr8nJlSmxPJiVQoKxZ5gPrl+YR7Upkr/Fm4+58wfIHDCIWQMGUVNZwexPPqKwsoYvvl/Ed/O+47RTJzJ+xtndXhFsW/YDu9evYeIV12qd/0mGNgPQOGoaXT7GzFtPS4QOhGBMpI3PRmZ3qn//8eALqmQsWseEgJNPZpzarnW3NDfzzccfsq1wN6qiYEPlvIsvYWDu8Ha9T3sR8Pt56bbriU5O4fJHnsBwgixqa+zPoWYA3dc9Q6Nbsq2yhdGPf4d/WRVKlYcLE6P5YHjfHtP5AxgUgVENYggG273uiMhIrrjxZn7z4O8YkpmBK6jy/ief8vrzz+Fxu9v9fsfDrjUree3en+NuaWbizGu1zv8kRDMBaRxESb2L297Op7TBTVa8jcy40M7ZXbVO1pY07r3ulr5J/GFQ7y7Z4HU8CCFICngplh0nt9ls4bIbbmJqZQXv/vdlCmvq+PtfnuCcM89ixMT2nXUcC9vzlvD1M08Rl5bOhfc9TO8eunitcXxoMwCNg9hR3cLGsmYaXX5WFzfyyZoyVhTVI4G7ZvRD6kMdZ2FFCy2+9h9FdwZJQtIgOn78E5ecwh0P/Z7Tx49BlZLP587jpb8/haO5qcPvfSB+r4dNi+bz9oP38uU//kJy3xyu+OOTZI8Z3+myaHQPtDUAjYOQUrJkZx1frCvjo/xSVAnZiXY+/eUEIswGlpc18suP11Jf7kSx6bnx9GweGt+nR+2QvXrejywUFnZPyUWv75yJcEtjI+++8iLlDje6YIDpUyZxyukzOvSe1UW7WP/dt1QV7qRmdyFBv5+4XhkMnnw6uTPOwWju3t5aGu2DFgxOo1VWFtXz97nbKGt04w9IAqqKL6DiD0o8gSB7/jyMeoX5v5pMeuxPydj/L383//p2G74WP/ZEK09eNIRzsxK6qCVtp6C+kan5O+gd8PLD2Z1vjlm1eBHfzptHQKcn0WLk6lt/QVRsbIfc64VZ1+BqaqTXoCEkZfUja8Ro0gcP6/aeSRrti6YANFrl3vfX8umasv2OxVgNjO0TS/+kCMxGHdkJdkZnxhJrMx5U3hMI8uu5W/h6WQnSr9KvfyxPnDkQnSKodftQJZzZOw5dNwoHce6cJazVmfliYCqj0lK6RAa308m7r7xIcX0TihpgyinjmXTO+e1+n1fuvJmm6iqu+OOT9BowuN3r1+gZaApAYz88/iC761wU1jp5dUkhZQ1uyhr391IZ3TuGd2eNx9CGzruoycWszzewbUst4oA/qczsGJ67ZBhDY+3t2YRj4ottBcwqb+HioIMXpnf9Yuy6vGV89fXX+BU98UYdV90yi9jEY9+XcCAv/fJGTFYrV/35HxiMpnarV6NnoW0E09hLVbOHqU8vxHWEBdzqFi9qGwcImVFW5l43ju+K6/hgQzk2o54os56F22so2tHAeU8twhBtJDsjmplDUzi9dxwyKEmNMGHUd87mI5/Px++3FEFELI+MGdYp9zwSueNOYUDuCN7/78vsqq7l3889x8SRw5l20aXtYqZR9DriMzK1zl+jVdqSEcwMLAZMhBTGR1LKR4UQuYTSQNqBIuBqKeVBQWCEEEVACxAEAnu0kBDiD8CtQE340gellN8cThZtBtA+VLd4OP3vi2jxBPYeu2REGmcPTUGvE0SaDcTZjKTHWtEpx9cJqarK59uq+WxLJWsK62mudcM+f3ISEEYFvVmHyRK6b06inXP6J3JudiImw/EpB4c/yKKyBhYU1rGqsIZd1W5Esw+DAZ47ty9njx1wXPW3J1vWrubTTz/FJ3RE6+DaWbcRl3TsswE1GOSVO28hKasvF973cDtKqtHTOGYTkAgNQ2xSSocQwgD8CNwNPAfcJ6VcJIS4CegjpXyklfJFwGgpZe0Bx/8AOMLJ5duEpgDaTmWTh8/XluHyBYm1GclKsDEsLZooayhUg8sXYGVRA6/+WMii7TWM6xPL+z/v+EBglU4P/15VTGWzB3QKjQ4vTS4/zU4fDqcfp9uPdAcRADpBdIKVWyZncUn/JJq8AVz+IL5AkGBQhvSIBL8qKXd5KWx0U9LoprzRTXWDm4YmLz6HD6GGb64I7DEmrFEGqne1AHDXmEiK653sqPUSkGA1KNiNCpFmHZFmA9FWIzF2M5mJUUwf0fG5APx+Hx++9irbyyowySB3//YBrLa2mc6klAQDAXR6PS21NSx84xV2rFjK+b/6HTnjJnao3Brdm3ZZAxBCWAkpgNuAeUBUOA9wOjBHSjmolTJFaAqg03n4sw28tbz4oOOn9Yvn7CEpzBicRE2Ll+teXYFeEbw/6xQy4qyt1NS5qFKyps7Bp1srWVZQR8GWuqOvRIDebiAq0kSvOCtDkiKZ1ieO0zJi9pqb/vbpcl5dUYVbhqygMTofRkXiUQVeVcErdUj2n/18/YtRDM5MPu42toXvv/ycxflrGJaVySXX3XDE66uLdvHhnx7E43RgstrwupwIRWHSVTcw6ryLNa+fk5zjWgMQQuiAfCAbeF5KmSeE2AhcAHwOXA6kH6K4BOYKISTwopTypX3O3SGEuA5YBfxaStnQyr1nAbMAMjJ6dsz1zuT2qdlICVsrW8jf/dPPWlLv4sFPN/DgpxsAiLYaeOeW7tH5AyhCMCo+glGnRsCp/Vhc0sBTC3aQEGnCatRh1OvQ6wSKIiD0D50iSDAbyY61MiDWRmaUBf0RFq7vv3g8vzzLS35BBcP6JBNt2z8MgqqqNDjc/OfrFby8zsXgGJWBGYkd2PL9mXreBeStWMGWHTsJBPzo9YcPsudoqMPjdACQPfYUkrP6kT5kGHFph/pvqaFx9DOAaOBT4E4gADwLxAFfAHdJKeNaKZMqpSwXQiQSmjXcKaVcLIRIAmoJKYjHgBQp5U2Hu782Azg2pJRc8sJS1hQ3UvDnc9hW2cKi7TVYjTrOG5ZCnF1bIGyN6oYWJj81H4lg5SNnEWHt3N/pu88+4ce160mxWbj11/cf1vwkpeS1e39BQ0UZ1z75LImZWZ0oqUZ3p12CwUkpG4GFwFlSyq1SyhlSylHAu0DBIcqUh9+rCSmPseHvVVLKoJRSBV7ec1yj/XB4Azz17VbOfuYH1hQ30i/Rjk4RDEqN5LYpfbl+QqbW+R8GRVHQKwKP1PNV3uZOv//0iy4hPSaSCqebl/7+FOphgtcJIZjxi7sAWPPtVwQD/s4SU6MHc0QFIIRICI/8EUJYgOnA1vCIHiGEAjxMyCPowLI2IUTEns/ADGBj+Pu+O3Au3nNc4/gpb3TzwcoSbnptJf+3sIBoq4HfnT2At24Z19Wi9Sjio2x8fc9kInR+np5fhMPl6XQZbrzzHnrHRlHp9PDi009xuBl7SnYOqTkD2bhgLks+eLsTpdToqbRlDSAFeD28DqAAH0gpvxJC3C2EuD18zSfAaxAy+QCvSCnPAZKAT8MLUHrgHSnlt+EyTwkhhhMyARUBP2+fJp3cVLd4mP6PRXt9/K8Zn8HjFw3tYql6LhkJ0YxLM/NdcZAF63Zx/ikH+Tl0KIqicP0dd/PGv5+lqL6RxbO/ZvI55+FoqGfnyuU011ThamrE2dRIQ3kpTdVVAEQndc5itUbP5ogKQEq5HhjRyvFngGdaOV4OnBP+vAvIPUS91x6tsBpHxqAo+23w2lTeM/LzdmcCwZAf6aShfbrk/oqiMPOmm3nq6b+zauF8atbkUbxhHVKq6PR6LFHRWCOjSOqbw9DTzyJ90FBSc7rP/gaN7ou2E/gE44bXVuz3PbdXNP6g2qZwDhqtkx1vYWGZi+pmN1GdnOtYSklNTQ1fffIxAP66GhpczYy7+HIGTJxCbFovzcVT45jRFMAJxozByawr/SnW/P+WFvHW8t0MTIlkYnY8t03uu3czmEbbcPtDM6oDXUU7mqqqKj799FMqKysRQmAN+pl5z330HpKrdfoa7YKmAE4wbp+azW2T+1Ln9FHa4GJXjZOdNQ6+31LNfxYVoFcE953Zv6vF7PZUN7QQlJIIs5F15U50KMTaO08BbN26lY8++giTycTZZ5/NoEGDiIiI6LT7a5wcaArgBKLO4eWhTzcSadETYTYgZWhnrS+o0uj2YdAJpvTv/vH6u5Ky2iYe+3A583b7kICCJICeYfoKvv12Nn379mXAgEPb110uF16vl5iYmKO+9/r168nPz6exsZGmpiYiIyOZNWsWdnvXR1HVODHRFMAJxHPf7+TbTZX7HYsw6zHqFPonR/DStQPITY/uGuG6Me8uXMdLPxTi9qvU+vQEURgRq2IWAWobm8lUGkhRmlm5spSVK1diMpnIysqif//+xMTE4HQ62bRpE4WFhbhcLgD0ej1JSUnk5uYyYsQIDIb9zW6NjY3Mnj2bkpIS3G73fu6dw4YNIyoqimHDhmmdv0aHouUD6KYUNRXx8Y6Pmbd7HopQmNxrMlPTpzIiaQQGpXUbvjcQ5PznfqSswY0z7AkUadYzIDmS/skR5CRH0C/RTk5SRKvJXY6FdRWFvL72W1x+J+6AB3fQjTfowRv04Fc96BUjZp0Vi86GVW/FarBhN9iwG+1EmexEmSOY3GcAvWO6bmYy6vefU+fTkxMRICVCzy+mD+KUQT95/ASDQRoaGqitrSU/P5+CggJUVT2oHkVRSExMxGq1Ul5ejsfz076B6OhocnJySElJIT8/n9LSUgB0Oh3BfTZ4DRkyhMsuu6wDW6txMqIlhOlB/FD6A79e9Gv8QT+npJ6CRLKiYgU+1UdOTA53jriTzMhM0uxpGHQHKwO3L8jmimY2lzextbKFbeFXi/en8M8jMqI5d2gKl4zsdczK4Pm8T/nPlj/wU7hNkKoeIY2hF0YkAaTwIBUPQhzcaQJIKbCofRkaM47z+5/GOTljMOnbR0G1hfP+8hm7WyD/j+dhNBx5UiylpKioiB07duByuTAajfTr14/s7Oz9FmcbGhpYvnw527Zto7Gxcb867HY7Z5xxBrm5uXz77bcsX76ciy66iOHDh7dz6zQ0NAXQY/ho+0c8vvxxcmJyeHbasyTbQht6XH4X84vn89jyx3AHQpm7FKGQYkuhV0Qv0iPSSY9Ip5e9F/GWeOIscaTYUjDqQh2plJLKZg87qhxsKGvimw0VbCpvJtZm5OnLhzFtwNHFnfcHgox4cywyaObeYY9z4aBRRFtsGHStx++XUuLye6h1NdPgaqHe00yD20GNs4klJavZ3JSHT1cSulg1Eq/vT27CaCalj2NgfCZxtigiTaHkMQLBb+e+zLb6bVzc/2wmZQ4lK/bY4+b/9rV5vL/Nx69OieGuCycccz2Hw+/3s3btWqqqqhgzZgxJSUkEAgHWrFnD0qVLaWho4MYbb6R3794dcn+NkxtNAXRzpJT8e+2/eWn9S0xMm8jfJ/8dm8F20HV17jqKmosoc5RR0lJCcXMxpS2llLSU0ODdP5iqQJBgTSDNnkaaPY2sqCz6x/ZnQOwAEiwJbK1sYdabqyhv9LDsgWkkRppZX1HCGyvXUFDbQHKUiez4WAYlJTMirTfJEVF76y5uqOWsD2eiGBpZf8MqFHH8+wy211Tw3oaFLCnLo9yzEYxVbS8ctGMT6aRY+pAT059RKQOZlDmE5MjDe84sXLuTO97fgEfqefP64ZwysOOjZwaDQZYsWUJ+fj5NTU2kpqZy2mmnMXDgwA6/t8bJiaYAujFSSv6R/w/+t+l/XNrvUh4a/9Ah7fyHw+FzUOoopd5dT427hnJHOWWOsr2vCmfF3mtjzbGk2dPYtPFUmhp6k55cS0D6qahKIOQbEAT2H80LnQeL2Y05Ng+/fSEACkYmpo2jd2Rv+kX3o39sf1LtqdgMNgyK4Zj91YOqZG1ZKfOLllPlrMHhc+AN+glKCVLFFXDROzKDnNhM1lVvo7B5J7Xe3XhEGUIJhH9XBSWQQISSTmZkNuN6Dea0jKHkJvdBURTeX7yBh74pxKyovHTtKCZ0QucPsHDhQhYuXEhWVhbjxo0jJydH8+vX6FA0BdCNWVSyiDu+vwOA7OhsfEEf3qAXKSWT0ifxyPhH2mWE7fA52N6wna31W9nWsI1KZyVlpf3YuG0gOoMDRUj6pfn41bQRTMkaRHFjA2vLi9lSVcWuukbKGtzUtqg4la3o4+cc1b0VFKJMUSycubBDs2oFggFWle9kWckGNtZspailgHr/bgLKPvmIVDMGGYWzZjTRjrG8c9sk+qbGd5hMe5BSsnDhQhYtWsSgQYOYOXNmh99TQwM0BdCt2VS3iT/n/ZkIYwQWnQWjzohRZ+SznZ9h0Vv48Wc/7rXldzeklFQ4K9hYu5Ft9dsodZTS4G3AH/TjV/0E1AB+1c/Oxp2oUuXM3mfy9JQ2J4FrNypbGvm+YD35FZvYVLeOsuASAGafvZheiUfvs38szJ49m7y8PHJzczn//PPR6zUvbI3O4bgygml0LIPjBvP2OQeH751TNIczep/RbTt/CMWhT7WnkmpPZUbmjENe5w64OfOjM5mzew6/avkVqRGpnSglJEdEc9XwSVw6ZBw//+TnlLkhMZjYaZ1/TU0NeXl5AEybNk3r/DW6BVqEsG5MZmQmJS0lXS1Gu2DRW7hjRMjMdeHnF/Lpjk87XYbq5moue/cy8t35TDRO5Ourv+6U+65cuZIXXngBgIEDB2KzHby4r6HRFWjDkG5Kk7eJSmcluYmtRtPukczsP5PCpkLe2fIOv1/6e55c8ST9Y/tzesbpXJB9AdGm6A65b1l9Gfd9fR/bA9vxCz/XJ1/Pr8/8dacsvJaXl/P11yFFc/PNN5OeruXo1eg+HHENQAhhBhYDJkIK4yMp5aNCiFxCWcDshBK6XC2lPCj4vBCiCGgh5FYS2GOHEkLEAu8DmeHyM1tLCr8vJ8IagCpVSlpKcAfcGBUjBp0Bk86EXtEj+KlD+u3i37KyaiXvnvsuA2JPrNjuVc4qfvfD79hYt3HvngaAofFDeXbqs8Rb27Yg2+CqpN5VTp/Y4QghqGwppKh+HcVN26hoKcaotzMyeSIfr/mRb5tDeYieGPoEF4y8oEPa1RrNzc28//77lJWVYbVaue+++zp0EVxDozWOZw3AC0yTUjqEEAbgRyHEbOA54D4p5SIhxE3A/cAjh6hjqpSy9oBjDwDzpZR/FUI8EP7+27Y2qCdR2FTIu1vfZUvdFrY1bNuv0zscf5rwpxOu8wdIsiXx6lmvAlDuKOeTHZ/w5a4v2VC7gQs+v4ClVy7de225o5xl5ctYX7OewuZCKp2VNHobUYMuvDKkMCN1Arcq8bc2ltk2e+/HZ0f/k6mDp3do2w7EbrczatQoysrKiI2N1Tp/jW7FUXkBCSGswI/AbcA8IEpKKYUQ6cAcKeVB+fLCM4DRByoAIcQ2YIqUsiKcH3ihlPKwcYp76gzg8eWP8/629xmZOJKBcQPpH9OfSGMkPjXk7ukL+gioYd91Qs8jPSKdSb0mdaXYnc7Nc25mReUKMiMzafG10OBtQJX7h4/QCR12g50UpY4ck4oxciwlLcUkWuJItafRK7IPmdGDSYnKptFVyp+X/YHVTaE/vduiIdM4mdxht5OW1jmbrj744AM2b95MRkYGl156KVFRUUcupKHRzhyXF1A4H3A+kA08L6XME0JsBC4APgcuBw5l3JTAXCGEBF6UUr4UPp4kpawACCuBxKNqUQ/CpDNh1pl5/ezXu1qUbs3dI+/m6m+upqi5CINiIMmaRFZUFoPjBjMqaRTDE4djNVipbNzImlUXUqfvyzVT3jxkfUkRffnv+fP4sfB9fI4KZPV36PWz2bxlDqvXDCKrz00MHHh+h47KS0pCi/j9+vU7KCKohkZXc7QzgGjgU+BOIAA8C8QBXwB3SSnjWimTKqUsD3fw84A7pZSLhRCNUsrofa5rkFIe5JMnhJgFzALIyMgYtXv37qNoXvfgbyv/xhub32DF1Suw6Ds3pWBPo9JZiVlnJtoc3ep5KVU+XXQqpkAV2UPfpH/S0cXuqa3dyPoNz+H3L0av9+H3n8JZZ77VDpK3TnFxMd9//z1FRUXo9XqGDh3K2LFjSUlJ6bB7amgcSLvsA5BSNgohFgJnSSmfBmaEK88Bzj1EmfLwe7UQ4lNgLKFF5SohRMo+JqDqQ5R/CXgJQiago5G3K/EFfXxf/D0f7/iY5RXLiTZF77fIq9E6e4LfHYqCqoVEqVWUmUZy7lF2/gDx8UMYNvQB8vI2odNVEB01/BglbRsZGRnccMMNVFVVsWLFCtatW8eaNWvIyMhg3LhxDBgwAN0hAuhpaHQ0R1QAQogEwB/u/C3AdOBJIURiuFNXgIcJeQQdWNYGKFLKlvDnGcCfwqe/AK4H/hp+/7xdWtTFVDoreWPzG3xZ8CWN3kZSbancPvx2Ls6+GLO+c3PKnohYrVl4VbDrWg8tfSRKS1ewfsMsDEYX8fEPMzz3xnaWsHWSkpI4//zzmT59OmvWrGHFihV8+OGHREREMGbMGEaNGqXtD9DodNriBjoMeJ1QZDAF+EBK+SchxN3A7eHLPgF+F14QTgVekVKeI4TIImQygpCyeUdK+US43jjgAyADKAYul1LWH06WnrAI/KuFv2Le7nnM6D2DS/tdyvjU8e0Sx0fjJ16ZN55kUc/ZUzeiO4pd0lVVa1i77mqkqqP/gGfpkzm1A6U8PKqqsmPHDvLy8ti1axcA99xzD9HR0V0mk8aJyzGbgKSU64ERrRx/BnimlePlwDnhz7uAVncySSnrgNOPKHkPQyd0pNnT+PuUv3e1KCcscQlnYal/k/XFHzCizzVtKtPYWMiatTcgpcLAAa+RmXnQ/4VORVEUoqKi9pp/oqKitBmARqej7QRuR/yqn/U16+kT3efIF2scM5NzfsnSpW+yo+SdNikAt7uOZcuvRFG8ZGQ82+Wdf21tLQsXLmTjxo2YzWamT5/O2LFjNS8hjU5HUwDtyL/y/0W5s5yHxj+099jqqtV8U/gNvqCPCGMEdqOdSGMkAEvLl2I32MmMzCQzKpOsqCxyYrTY8EdCygCqBJtvO1LKw/5efr+bhYuuQK+vIy72jwwaeOiAdR1JZWUlW7dupaioiKKiIgwGA6eddhoTJkzAYtE8wzS6Bk0BtBMBNcAbm98AQm6f72x9B1/Qx8rKlVj0FiIMEbT4W/bbBZxoScSgMzC7cPbeDWDD4ofx4hkvYjfau6QdXU0g6GNZwWtU1S9F9dejBp1I1Y2QHhTpQy8DWEQAmw4qOPzWEVUN8v3312A0FmIy3cHIkVd1Uiv2p7CwkDfeeAMpJUlJSUyePJnRo0cTEXH4bGUaGh2NpgDaCb2i57djfsvC0oVEGiMpd5TT7GsObW4aePVe/3+/6sfpc9LibyHZloxBMeAJeChuKWZhyUKeW/Mcv1/6e/4x5R9d26BORkrJ3C3P0Fz2IrE6H1ESnKrAK3UEhQEVI1KJQugs+AxRDM68ntN7XXjYOhcsvB29YS3Iyzh14r2d05BWyM/PR0rJXXfdRWxsbJfJoaFxIJoCaEeuGXQN1ww6vE3aoBiINkfvt9HJrDeTE5NDgycUC2/e7nnM2z2PiakTsRqsHSnyQXg8DhSpYLR0zn2dvkaWbn+OuspPSFCaUYSRQMKNTMy5HZsx+pjNYXl5jwPz8PlO48wZf2lfoY+S0aNHs2nTJubMmcPMmTM1v3+NboOWEawb4Vf9vLLhFV5Z/wo+1cfQ+KG8c+47nXLv+uId5D3zCAnz12HxQEuEgjPGgi8+Em+UBQmIoIoIqhAMogRVCKrIYCD0XQVFBZ0qUXUKQYNC0KAjYND99NmoYLJGEBmZQGRWCi2x7+LHi0WR1Ab0mGPP5Ozcv2I8TqW3YeOrVFc/gcs5kLPO+gSDoesT6ixfvpxvv/2WqKgoTjnlFEaMGIHJZOpqsTROErSMYD0Ag2LgttzbiDXF8nje4wTUAHXuOuIsB0XYaFfW5X2J/+e/Jd0r2T0qBdG7F8GqanTVDVhLakjYoiIFqIpAVULvUicI6gQoCuh0BBXC50DvkZj9Kga/ii4gMfhV9P7QuxIeb5T/nw89P/0BZlRGYqkqomj3A9jiBhOZMRpb+jCUo/SMKSz8lsrKv+BxpzFt2lvdovMHGDduHDExMSxZsoRvv/2WJUuWMGvWLG0dQKNL0WYA3RCX38WDPz7I/OL5ANwx/A5+nvvzdr9PQ00p6977P4xvfI6CIPa/z5MzbHK732dfpN9PfX05hdu+RHUsxuzQ4QmU4TXU449wI/fpr4UX9E0mTL4o9MZo9IZIDKZoDNY4zLZ0FEx4GgtwO0rw+qtxigbcMY14vTa250/H5lHQCYGCQCcERiEwKAJVpyOAwBcM4ldVVDWIKiV7/i9IQoGujMEgZglmnQ6j0YDRZMJoMmGyWjFaLFgjIxk0/QzMMdF7ZVZVlfqiIkrXrKFq925cbg9SDRIXF8eQadOIGzCAgoIC3nzzTUaNGsV5552neX1pdDhaUvgehJSSwuZCXtv4Gp/t/AyjYiTv6jz0yrFP2OqLd1CwbA4Nq/OQOwqxlzUS2xQEoCrRQOY/nyVz1JR2asGxoapBXFXbaCleiaN6A07nLjxqBT5DE0G9D2mSyNaiaaigaxYIt4VyJYnKgmHYmvWohLIQqRKCAvyKgl+nQ1FV9IEABlVFDyEloSggwtGahEAvBH5FwS0lXiAgBH6dDtlK5FCD348hGEQVgoBOR2CffL/6QAAJBMPHEhwO0mJiWOv3AzA1PZ3JN9/cvj+khsYBaCagHkCDp4FXN77K/OL5e3MBD44bzMPjHz6qzt/ZVMvO5XOoXrmEwKatRBdUE90cxA6YFahKMtIwIAVHVh8SJ09n0qTLukWiEkXRYU8ZhD3loLQSAKgeD4G6ary15TgaNwMSa0J/rL0GY4js+OTuUkoCHg+ehgY8DQ1UFRRQtGEDHlXi0+vRCYFeUYiLiSGtf3/SRo3CEhuLlJLKDRtYv3AR2zwe1vp8WL1e3EYjzo8+wtGvH/ZJJ1fuB43ugTYD6Ea8sO4F/m/t/wFwLsMYts1LdEENluom7E1+aqIE/rR4pF4HBj3SYkbRGxB6PdYaB2nLCgBQBXtt7TWxOhr7JqIbMoCEURPoP+5MoiISuqqJJzX+6mp8u3bh2bqVlm/n4F67FoDe77yNdeTIrhVO44RGmwH0AH7W/2eUtpQS9crnnL98NRAyz7gSozD4mogIKIiiBpRgeFHVp6ILSnTqTx0+hD43/OZ6+p55KQPT+nVRazT2EGxqovLxJ2j+8su9x4y9e5P0uweIuugidFqWMI0uQlMA3YgYcwwPKudSvPwTANLnfcPA9LbFFVL9fny7d1P6y9vxFxcT87c3cM9bj/vB32EZOrQjxdY4AjXPPEvzl18Sd+st2CZMwJSdjS4+Xlv81ehyut7wqwGE7MuNH31Eyc9/galfP7IXL8Lexs4fQDEYMGdnkz13Dkm//z26uFjca9ZQdPlMCq+4AtXl6kDpNQ6FVFVUpxOA+F/8Atspp6BPSNA6f41ugTYD6GKklDgWLqT2uX/j2bwZY3Zfer/5BrrjiAsfe9WVxF51Jc4VK6h4+BE869azY9rp9P12Nnot3nyHEnQ48WxYj6+4hJb53+FasRLp8aBPSkJYO3dXt4bGkdAWgTsQ1e0GRUFpZcen6vXS9PnnNLz5Jt4dOzGkpxP/i58Tdf75CGP7bl4qvvc+nLO/piU2m5TH/kja1OHdwuvnRMOzbTu7r7pq74hfn5pCxLTTMWVnYzv1VIy90rpYQo2TlWNeBBZCmAnl8DWFr/9ISvmoECKXUBpIO1AEXC2lbD5EHTpgFVAmpTwvfOwPwK1ATfiyB6WU3xxlu7ol0uej5Be/wLl0GSgKhrQ0TH37YsruGxoFqpKmL7/Av7sY06CBpPzlL0Seew5KO3f8ENqYVLWxBDvQZEpm5YeN2N75mH45BkbeMg1LXGS73/NkRPV6qXz0UVSnk7Rnn8E8YACGtDSEFvdHoxvTFhOQF5gmpXQIIQzAj0KI2cBzwH1SykVCiJuA+4FHDlHH3cAW4MDe5p/h5PInFJ5t23EuXYYuPp6YK67Au6sA384CHD/+CIEAAIbeGaS/8gq2iRM61B5cPnsJ9pL1+M+9gYn330r0Oz+wY5OPtYVxFD84mytfvKLD7n0yUfHII7jXriX1yb8SOaNrcg5oaBwtbUkJKQFH+Ksh/JJAf0IzA4B5wBxaUQBCiF7AucATwK+OX+TuTaChgbJ770WfkEDmRx9iSErae06qKuwxuSlKu3f8qqqy5cGn8azOR7FaUYUO89blqDozvW68DGtyLGN+dSFjgPn3/Y+tjgwWPfoek//4s3aV42RDqiotc+cBoE9K7mJpNDTaTpsWgcMmnHwgG3heSpknhNgIXAB8DlwOpB+i+L+A3wCtRb26QwhxHSHz0K+llA2t3HsWMAsgIyOjLeJ2GoG6OhSzGWG1IoRA+nyU3XU3gepqer/15n6dP4BoR7u7Ggyy8+VPaf5hCUp0HJZ+WTi//w7b9mUo9iRkrcToa8E96kz6PXg7UYOy9it/6sOX0fS7z9hYmUz//B0kj9L2C7QFKSXB2lq8Bbtwr1mNe9Mm1OYWpMcDQPENN5B4//3E3XxTF0uqoXFk2qQApJRBYLgQIhr4VAgxBLgJeFYI8XvgC8B3YDkhxHlAtZQyXwgx5YDTLwCPEZpNPAb8PVzngfd+CXgJQovAbWpVOyKlJNjYSKCqCn9FBYHKSvyVVXg2b8b5ww8ACKMRXUwMQq/HX1ZG6t+ewjJsWLvLoqoqu96dS8uK1ajrV2Kt2IpZZ0IX9MJ8sAGOUecy6o2nUHTKYdMlmqLtnPGbabz957Us+e9KzstKxhSjRaY8HM7ly6l4+BH8paWhA0JgzMxEHxeHdexYZCCAe/169AnxXSuohkYbOWovICHEo4BzX9u9ECIHeEtKOfaAa/8CXEsouKKZ0BrAJ1LKaw64LhP4Sko55HD37ggvoKDDib+sjEBlBf6KSvxVlQQqKvFXVoY7+8q9o7u96HTokxKJOH06hqREAg0NBBsaCTY0YDvtVGKv6pjUg5seexHl7X8B4LXGwdQLGPaXe1H9fhrWbKOloIS0y87EZGt7nPkFD7zB5sZe6IIeIoINuIUdKQRG4cegBDDpVYz6IIpUEUYTwmwkMsZITLKViORoItJisWckobe2FqXtxEH6/WwfNx5dbCyx112HMSsLy9Ah2i5ejR7B8XgBJQB+KWWjEMICTAeeFEIkSimrhRAK8DAhj6D9kFL+DvhduJ4phBaNrwl/T5FSVoQvvRjYeEwtO0qklLTMmYtjwQLcGzbgKyz8yS4PoCjoExMxJCVhGjAA+5Qp6JOTMCSnYEhJRp+cjD4+vku8O7z5K7AAWcuWY4r5qeNRjAYSTh1BwqkjjrrOyX++ht7frGDLd+U4PHqSTC2IYACfD3x+BafPQKNiQwoF6VCRKPhrLbBDAg2hl9xBglLLhU9egCnyxPR1d+atQHW5iLroQmKvu7arxdHQaBfaYgJKAV4PrwMowAdSyq+EEHcLIW4PX/MJ8BqAECIVeEVKec4R6n1KCDGckAmoCGj/gPetEQxSds89ANinTiXy3HMwZWWhT0oOdfAJCQh999wfpzpa8FjjMUS1n6lGURSyzhtP1nnj2yaDy4WjtIbG3bU4Kptw1DqoLGyhxJfCa79aQKyuidjIIJFxJqLTY0ga05+onO61dnO0uPLzKf3lLzH27UvMNYdP+amh0ZM4KTeCFc+ahXfrNvotXtQOUnUeGx59Hv37/8Y5ZCqJN11LrzPHoei6x4aunZ8uYdviIuqa9DgMsUgRniFJlYkj/Az/xdldK+Ax0vT115T/5rcY0tLIfP899DEdH3ZaQ6O90RLC7EPR1dcQbGyk79dftYNUnUcwqLL2xt9iXvktigzgscYjR0wk+bLzSJ5xCko32XSkBlWaSuup21RM3ifbaVZimDBeR+5N07tatKPCt3s3uy66GFN2NhmvvKzZ+zV6LFo4aMC1ejW1//437vx8kh58sKvFOWp0OoVRb/wNT9UD7H7rS4Lz5mFZ+jVNSz6nxhxFcNgEIqZORRU6vIWFGCLt9L/32k4P+6DoFGJ6xxPTO57kUVl8+eBXLFmeSPLIQpKGtz3AXVfT9OVXSI+HXs89q3X+GickJ4UCUH0+Kv/0J5o++hhdfDyJ999HzNUd46nTGZiT4uj/6xvg1zfgqmqg6K2v8H//HeZV3xFYMRsI7dYDaD5nMtEDM7tKVOxJ0Zx7/0Te+9sGvn1+NTP/HIMlIbrL5DkaFKs15CAguoeZTUOjvTkpTEBbBgwEwDRgAJnvvoNisbS3aN0Cb0MLtYtWoDMZqPh8HuaFH+GZcQ0jnn2oq0Vj6/9m8/0yPUZfC73ttQw8ayBpZ03o1rFydl9/A67Vq+m3aCH62NiuFkdD45g5qU1AppwcvNu34926lZJbZyEsFvSxMRh698Y6YgSW3NzQaK+HY4qJIO2i0wEoe+09ACIHZXelSHsZcMPZ6BJWs3ZuM9s9WWz/0k/kB+/SJ0My+PLxxAzrfjuR/aWlGHtnaJ2/xgnLSTED8JeXU/3PfxFsbkK6PahuN4HaWgKVlaEpvl6PZfBgrGNGYxk1CuvIkT3a5htwe9k2ciQBexy5KxcfuUAn46h1sOm9ZezY0ESTiAWpkhAoI3tYNIOunoQ5vnt42lQ+9jgNb79Nvx9/QB+v7e7V6LloXkCtEGxpwb1mDa5V+bhWrcK9YQP4/SAE5mFDibvlFiLPOKPd7tdZFH04D/cjdwEQ+fybpJ1+0HPvNtRsKmHjRyspLBG49VHogh5SjTUMmJhG34tPRWfpmh3GqtvNthGhRO3ZC77HkJLSJXJoaLQHmgJoA6rHg3vdelwrV9L8zTf4du0i4Z67ifv5z3tUCj/V62PTTXejz18IQEBvBQFIidTpUc68jP6P3oXB1n3CN6hBlaJ5a9kydxulLVEEdGZMvkYyIuoZetkYUk7L7VR5fMXFFMw4E4ABWzb3qOevoXEgmgI4SqTPR8Ujj9D0+RdEX3EFyY883G13CB8KV2UdO555k0B5KagSiUCtKCWidB2O9FzGzHuvq0VsFb/Tw9bPVrA9r5IqbwwSwdBejZz64MWdtteh7pVXqH7676S//BL2007rlHtqaHQUmgI4BqSU1PzzX9S99BL2adNI+/vT3caDSEqJVCWqKlF0CorSthFq0B9g45jT8CVlMWbO2x0s5fHjqKhn9u+/olrXi+Hp9Ux86LIOvZ+UEu/WrRTPmoUxrReZ773boffT0OgMTmovoGNFCEHir+5Fn5hI1RNPUHzDjfT6zwsdEg4gGAiy48WPcSxfgfR6ET4PeFwQDCCFgs7RgOJuQVH9KAEfStCHIDSql0JBCoWg0UogKgmZlI4+IR4FlaDXR9DjC0XzVCC4uxCbpxHL+ee3exs6AntKLJe+eA0f/vID1u2OJe7dBQy4cmqH3a/2hReoffY5FJuN5D/+ocPuo6HRHdBmAG2kee5cyu+7H0NqKumvvIyxV692q7t4XQUtP5uOItX9jruj0kDRIRSBao2C6FhUxQBmCzqLBcWgQxESGQwS9AVQmxoR1WUYGsrRex2oQodU9EhFtzc2jy7gxpMxlJHfvNmjEsO765r58Dff4lQiOe/qVNKntH++BYCCc89DBvz0fu01DKmpHXIPDY3ORpsBHCeRM2agfy2Okl/eTtHPriT9xf9gGTy4Xepu3FWFIdz5BxUDihpAINF7mvEPnYhl6BAicgcRO3oI1vi2RQJVgyoBv4reqNvPPKSqao/q+PdgiYvkwgdP5b0/fcFH//kfkS/66NUrneSsbIxWG3qzBUNEBNa0XkT2ycIUEbF34VYGg0ivl6Dbjc/pwJiUjMHUes4Ey5DBOBYuQp+Y2JnN09DoErQZwFHiLSig+NZbURubSPzN/URdfDHKITqTo8HnCWA0h/RxwOWh4O3ZNH/6GZbda9EFQ8nWJAJX2mAy//UU8UN7Tkyd9kKqkrnPvc3GpaHFa6FK5CHWPhRVYgoGQUJQEQQUgRpWfIoqSbFF0Hf4KPpdcAnRWX33lmv+dg5l99xDxv/+h238uI5vlIZGJ6AtArcj/qpqSm+/Hc/GjeiTkoj/5S+JvuRihMFw5MJHSdAfoGF9AU35G2h86w2s1Tvwn38Tw/52f7vfq7vQWirLFV8sZNlHbxHwVgKg6CP55b+eoWbrZnxOBwG3G39TE+6aahw11ThbmvFIFUXRoTcaMRiNGIwmDAYDjcW7KWmsxaMPmcUipCAtNZ0+E04jc+p0dk+dhiExMRT+OSGh09uvodHeaAqgnZFS4srLo+Zfz+BeuxZDRgbxt91G5LnnoBiN7X4/1ednw9jTUI1WBn37KabYnrtT+VAUrt/Jkvc+pbpwJQA6gw2d3kgw4CPgrUExRNN//Az6jBxBar90oo4jqJwaDFL2/XwKvp9L6a4d1Kh+VEVBSIkxEGRYSTWjX3gJ6+juu4lOQ6OtHLMCEEKYgcWAidCawUdSykeFELmE0kDaCWX0ulpK2XyIOnTAKqBMSnle+Fgs8D6QGS4/U0rZcDhZupMC2IOUEseiRdT86xm8W7eii4kh7pZbiL3xBkQ72tpX3ft3bLNfQbn7Mfrf1rGukJ1JMBBg1VcLWD37G1yNOwCBPa4fZnsMXpeDgM+LzmDEFpPIGTdfTVKfjtmR662tofDLz9m1cjlb6irJ6TeI8x9/qkPupaHR2RzPIrAXmCaldAghDMCPQojZwHOEcvwuEkLcBNwPPHKIOu4GthBKCr+HB4D5Usq/CiEeCH//bdub1D0QQhAxZQr2SZNwLltG/euvU/23v+FcsoTUp//WLoHE6reXYZr7Ft6s4Qz7+SXtIHXX43G5+e7lt9i58nuC/haEzk7v4ecw5ZpLiE9P7nR5TPEJ5Fx3I0UeByysZMCFF3e6DBoanc0RFYAMTREc4a+G8EsC/QnNDADmAXNoRQEIIXoB5wJPAL/a59SFwJTw59eBhfRABbAHoSjYJ07ENmECjR9+SNXjT1D0syvp/dabGI7To2T3P17EpPpI//Pve6QHz4E0VNbxxm/uI+CtwWjLIveMa5h4xRkYze1vOmsrUkq+euZJduQtZfwlV5A9um05kjU0ejJt6k2EEDohxFqgGpgnpcwDNgIXhC+5HEg/RPF/Ab8B1AOOJ0kpKwDC7632kkKIWUKIVUKIVTU1NW0Rt0sRQhAzcyYZ//sfgZoayu6+B+nzHVedwepKvJZYoocPbCcpu5YF//uEgLeGcZfezR2vPMPU68/t0s5fDQb5/rX/sCNvKTnjJjLximu12D8aJwVtUgBSyqCUcjjQCxgrhBgC3ATcLoTIByKAg3o5IcR5QLWUMv9YBZRSviSlHC2lHJ3QgzwyrCNHkPrnJ3CvWUPlE38+5nqKP1+EZcsyZFpWO0rXNZTvKGb2/71D0bq5mGzpnDrzDEQbQ1h0FFJKvv/fS6yd8zWjz7+E8+7psZNQDY2j5qg2gkkpG4UQC4GzpJRPAzMAhBA5hMw8BzIRuEAIcQ5gBiKFEG9JKa8BqoQQKVLKCiFECqHZxQlF5Nln49m8mbqXX8E8eBAxM2e2uWxjQTk7//B3LKu+xW+NZcCLT3egpB3L6jk/8sPb/yXgDc3g9KZ4zrvnV0co1fEEAwG+f/U/rJ//LaPOu5jJ19zU1SJpaHQqR1QAQogEwB/u/C3AdOBJIUSilLJaCKEADxPyCNoPKeXvgN+F65lCaNH4mvDpL4Drgb+G3z8//uZ0PxLuuQfP5i1UPvY41jFjMPU58gaudQ8+g/L5/7CqPjyjziTnLw9gTe05s599aWloYcGrT6PobPQbdxlDpk0gc1h2l69lBHw+vvn30+zIW8rYiy7n1Cuu7VJ5NDS6grb8L0wBFggh1gMrCa0BfAVcKYTYDmwFyoHXAIQQqUKIb9pQ71+BM4QQO4Azwt9POIROR/If/wh+Py1z5h7x+l0P/R/GT/6DPrYv/mmPYhx+FY6KYCdI2jHMfv4tIMCEK27mgl/dQNbwnC7v/KWUfP704+zIW8qU627htCuvb1eXXQ2NnoK2EawDkUEVb1ETFb+7D8/6peiT0oi5+lYMaSn4y6oJNDXjLyrElJNDzM/OoexXf8SdPw8laRDq9F+h80iMARW9EHgUgTIuhdTJaZijW0/k4nMHqFxQglQEUdnRKHoFT6WTpuIWgmuqkBmRKJawE5cklCAm/B76HuocQ8L/dHxP2GkZUDHWN6PzB1nhzqPZ34ga8KEG/aiqHzXoR6p+pBpASj9IPyCJTBjCjf96HH03yaeQ//VnLHzjFaZcdyujzr2wq8XR0OhwtJ3Ax4lUJcEWH8EGD4F6D2qLH9UXRPqDSG8Q6VORviCqX0V6gwSbvQSbfMigivO7h5HOtnkwWSeeT6/n/oTOGurkXXVuip9dg9kTQC8EqpQ4TXoMA2Ox9YnE3+zDlGClelEpSpmDKF3nLKourv2GWn81is6Aojeg6Izo9EZ0BgM6vRG9wYjeaMQWE8sZt8zEZO0e2cd8Hjcv33EzZpuNm/71kubto3FSoCmAY8Bf48KxpBzvzkYCDR4IHvxbCYOCMCoIo27vSzEq6CKM6KJNGHtFYMqOBr1CsK4Z15pN+IrKMPVNw9grBX2vZBzf59GyYAlqUyPpLzyBYj44uFzQF6B8SQXN62vQVTixH3BeSolLp+BLjyR2aBwtRc0IVUUfb8UWb8GeZgdFIAQhzxtFIBTCx346LoQInw+5tAY8Hnb+dwERlRH4VA++ASo5V01Dbzn+AHidjaoGmf3vf7B1ySJmPvoX0gcN7WqRNDQ6BU0BHCX+ahfVz61B+lXMA2MxJFrRxZjRx5rRxZjQRRoRBl2XuDGqQZXa9TU072pCZzEQdPqJH5FAdHb7Jqqp+HETLV/uxioiqLNW0+eW04hM7Zlhkqt27WTuS89RXVhA7hlnM/2W27taJA2NTkPLB3CUeAubkH6VmEv7YRvT+aEJDoeiU0gckUTiiKQOqV/1B9n24jzsJVakqhKcYSH3jEs75F6dQXXRLt7/wwOYrFbOufM+Bkyc3NUiaWh0CzQFcAjM/WIQRoWmebuRUmIblYTQnfieIo2FZZS/vIpINZZaSyU5d52BNTa6q8U6ZoIBP/Nf/Q86o5Gr//Iv7DHHH5tJQ+NEQVMAh0Afaybh1mE0fllA4yc7cfxYTuyVAzCm2LpatA7B7/Wx5fU52HaasGDHmesj96rLevQiaTDgZ97Lz1O+bTNn/fJerfPX0DgATQEcBmN6BAm35eLZXEfDZzupeWEdcdcPwtw3uqtFa1eK5q7A+V0ZsUo8TmMzidcNIian/XIedzZSSnatXsGSD96mpmgX4y+5gsGTT+9qsTQ0uh2aAjgCQggsg+Mx9oqg5pUN1L6yAdv4FKLOzEQx9+yfr6WshqIXfyDGl4BJWAieYiLn/HO6PD7P8dBSX8vXzzxF2dbNRMQlcOF9D5M9RovsqaHRGj27B+tEdFEmEu8YTvOc3TiWlePZVEfU+X2xDInrkWaSou9XEfi2lgiiac5ood+N0zDYuoev/rGgBoNsW/YDS95/E1dzM2fMupMhU6aj6HRdLZqGRrdFUwBHgWLSE31BXyzDE2j8ZCf1b2/B1C+a6AuzMcRbulq8NrP19XlYNhtQhYrlil5kjszpapGOGTUYZOvSxSz/+D0aKsqI65XBpb/7I2kDBnW1aBoa3R5tH8AxIoMS5/JymubthqAk9or+WIbEd7VYR6Tk+zXIOc20GBrJvm86puieuah9YMefkJHJ+MuupN+YU7S4PhoaB6DtA2hnhE5gn5iGZUg8ta9tpPGrXZgHd29zUMXqzfi+rSGAn773Teuxnb/f6+HjP/+esq2bSejdhwt+/SDZo8drHb+GxlGiKYDjRBdlwpQVjWNZOdKvIozd0+bcuKuMpvd2YRQmkm4dgTk6oqtFOiY8Tgdf/uMvlG3bwpm33cPgSdO0jl9D4xjRFMBxIqXEs70BU58olG7Y+Qf9AXa+twjjBokJC4azYonOTu1qsY6JhspyPnvqMRorKzjrtns0104NjeNEUwDHiWtlFYFaNxFTupfffPnSjdT9uBNzrQmbYqdJV0fKDaOI7X+o1M3dm50rlzP7+X+g6HRc9vBjWiA3DY12oC0ZwczAYsAUvv4jKeWjQohcQlnA7EARcLWUsrktZcPn/gDcCuyJk/yglLItiWS6DY68Cho/34mpXzTWDorLc7R46prZ8fx8YlzxRMooWoyN6EZGMPDCC1B6aCiLZR+/y9IP3iYpqx8X/Op3RCb0zIB0GhrdjbbMALzANCmlQwhhAH4UQswGniOU4nGREOIm4H7gkbaUlVIuD5//Zzi3cI8iUO+heW4RrrU1mPvHEHvVAEQnxeE/FGogSOHnS2GFm0hiaExqJPumKfTuobZ+CJnXVn35CUs/eJsBEydz5m33oDcYulosDY0ThiMqABnyE3WEvxrCLwn0JzS6B5gHzOEABXCYsj0O1RvAs6Ue56oqvAWNoFOImJpO5PTeXdr5q/4Am5//FmO5glWJoEV6MF2UxJAJU7pMpvagatdOlrz/JoVr88k55TSt89fQ6ADatAYghNAB+UA28LyUMk8IsRG4gFAy98uBVo3LrZXd5/QdQojrgFXAr6WUDa2UnwXMAsjIyGhru44ZGZQE6t0EatwEalz4a9z4Sx34q5wgQRdjIvL0DGxjktFFdX1SlK2vziO6MoomYx2eEZBzwdnoemhHKaWkZNN68j77kOINazHZbEy+5iZGnXuR5umjodEBHNVGMCFENPApcCcQAJ4F4oAvgLuklHFtKSul3CiESAJqCc0IHgNSpJQ3He7+HbERTPpVPDsb8Gytx1fmwF/pgoC697xiN2BIsWHMiMSUFYWpT1S3ipWz7TdfEzQEGfj4+d16D8LhUNUgBfkrWPHZh1Tu3I41KppR515E7hnnYLJau1o8DY0eT7tsBJNSNgohFgJnhW33M8KV5wDntrUssFFKWbWPcC8DXx2NLMeLVCXN84tx/FCG9AURJh3GXnbs41MwpNjQJ1gwJFhRLN3XUUoGJVYRQX10bY/s/D1OBz+88z+2/LAQv9dDVFIy02/5JYMnT0dvNHa1eBoaJzxt8QJKAPzhDtwCTAeeFEIkSimrhRAK8DAhj6A2lQ2fS5FSVoQvvRjY2D5NOjKqJ0DFX1ciPQEsQ+OxjUnGlBWF0PcsM8PuRavQC4Ep8cAMwT2Dr5/9G8Ub1jLwtKn0GT6afmNP0YK3aWh0Im0Z3qYAr4dt+QrwgZTyKyHE3UKIPYlVPwFeAxBCpAKvSCnPOVTZcJmnhBDDCZmAioCft1ObjkjLD2VITwBj70jirh7YWbdtV5qLq2iZV0KkjKXvxad1tThHTdHafIrW5jP+0iuZOPPqrhZHQ+OkpC1eQOuBEa0cfwZ4ppXj5cA5hysbPnft0QrbXkRMTKVlfjHBJi/+SieG5J4VE6exoIzaFzcQJWJx9w9giOw5kUgBfG4Xc1/+N7GpvRh30eVdLY6GxklLz7J5tBOK1UDCL3ORAZWqf6+l8YuCkJdPD8Bd30zVi2tQ0GGamUL/m6Z3tUhHzdYli2mpreGMWXdotn4NjS6k+65wdjCmjEiS7h5J0zeFOJaW41hajj7JimVIPMZUO4pFhzDoECYd+hgzwtA9dGXRx8uIUKIITjWTNKp/V4tzTGzPW0J0cgppAwZ3tSgaGic1J60CANBFGIm9oj8RU9Px7mjAtaGWlvnFB1+ogD7BijHFhiHVjiHFhiHFhs7eeaNXd30zBa8vIqLSTrOoZ8AZ53favdsTj8NByab1jDrv4h7puaShcSJxUiuAPRgSrRgSrdgnpqF6AgRq3Ki+INIXRHqD+Ktc+CuceAubcK2t2VtOiTBiTLVhSPlJKejjzIh2irnjrKxn1+dLkSUeInwxRCvRNFhqybptMkoP3RhVkJ+HGgzSb+wpXS2KhsZJj6YADkAx6zGmHzp+TtDpx1/hDL8c+MudeHaUghreUKeEcgToY83oYszoo03ook3ookIvfbylTRvJir7OQy5yEKNE4ZF6nDEO4k/PYejYnufxsy87VizFHhdPct+em4ZSQ+NEQVMAR4nOZkCXHY05O3rvMRlQ8VeHZgmBOjeBeg/Beg+ebQ2oLb79ygujgiHFjikrCnO/aAzpESiGg33fA4sakahYZ6aTNqLfCWEu8XncFK1bzbDpZ50Q7dHQ6OloCqAdEHoFY6odY+rBG7JkQCXY5CXY5CPQ4MFf5sBX2kLLohJaFpTgV324Ay2oBFGMeqQARQoilBgcahNJPTBhu8fpoHDNKpqqq3A2NuBqbMDjbMHR0EDQ7ydn7MSuFlFDQwNNAXQ4Qq+gj7Ogj7NgIgpGhfIGqJ4Aru11rHzhXUw6K4rQoQR0KEKHQFChFGLPTWbX6pUoej2KogMkUpWYrFasUdGY7XaEotBQUY7H4SDg9xL0+Qn4fQT9fgJ+P0G/n2DAjyUykujEZKKSkrHHxoXraz+CAT+Fa/LZ8sMCClavIOj3A2Cy2bBGxWCxRxCVmETmsBGkDuiZm+80NE40jioYXFfTEcHgugPBQIAdeUtYO/cb6kqL8ThaOvR+Or2eyIQkopOSiU5OJSoxicjEJKISkrDHxGIwm9EbTa2aaVzNTTRWluNuacFRX0dTdSWNlRWUbN6Ax9GCNSqa/hNOY+DEKST07qP5+WtodAPaJRicRseg0+sZMHEyAyZOBsDv8+Ksr8fncaMGAgQDAdRgACEUEOB1uXA1N+JxOFCDQaSqkpozEIPZhN5gRGcwojcY0O156fQ4mxppqqoMddhVFeFXJWXbNuNzuw8WSgiMZjMGkxmD2YwaVPG5nHicjv0uU3R6ohKT6DNiNAMnTqb3sBFaPB8NjR6CpgC6IQajiejklHatM9qcTHRS8kHHpZR4nI6wcqjC1dyI3+PB7/Xg97jxeTz4PR4URcFgsRKdlExsWi8sEZHYomM6xJykoaHROWgK4CRHCIHFHoHFHkFy335dLY6GhkYn0jN3E2loaGhoHDeaAtDQ0NA4SdEUgIaGhsZJiqYANDQ0NE5SjqgAhBBmIcQKIcQ6IcQmIcQfw8dzhRDLhBAbhBBfCiEi21o2fC5WCDFPCLEj/B7Tvk3T0NDQ0DgcbZkBeIFpUspcYDhwlhBiPPAK8ICUcijwKXD/UZQFeACYL6XsB8wPf9fQ0NDQ6CSOqABkiD27fwzhlwT6A4vDx+cBlx5FWYALgdfDn18HLjoG+TU0NDQ0jpE2rQEIIXRCiLVANTBPSpkHbAQuCF9yOZB+FGUBkqSUFQDh98RDlJ8lhFglhFhVU1PT2iUaGhoaGsfAUcUCEkJEEzL33AkEgGeBOOAL4C4pZVxbykopNwohGqWU0fucb5BSHnYdQAhRA+xus8DtTzxQ24X372i09vVstPb1bDqyfb2llAkHHjyqncBSykYhxELgLCnl08AMACFEDnBuW8sSmj1UCSFSpJQVQogUQjOEI93/oAZ0JkKIVa0FVDpR0NrXs9Ha17Ppiva1xQsoITx6RwhhAaYDW4UQieFjCvAw8J+2lg2f/gK4Pvz5euDz42mIhoaGhsbR0ZY1gBRggRBiPbCSkB3/K+BKIcR2Qh16OfAagBAiVQjxzRHKAvwVOEMIsQM4I/xdQ0NDQ6OTOKIJSEq5HhjRyvFngGdaOV4OnHO4suFzdcDpRylvV/NSVwvQwWjt69lo7evZdHr7elRCGA0NDQ2N9kMLBaGhoaFxkqIpAA0NDY2TlJNeAQgh3hdCrA2/isKb1hBCXL3P8bVCCFUIMfwoymcKIdz7nDvIS6oz6Kj2hc/9TgixUwixTQhxZqc1qg3ytbV94WvvDLdhkxDiqfCxE+L5ha89qH3h4z3++Qkh/iCEKNvnunPCx0+I53eo9oXPHf/zk1Jqr/AL+Dvw+1aODwV2HU15IBPY2NVt6sD2DQLWASagD1AA6Hpa+4CpwHeAKfw98UR6fodp34ny/P4A3NfK8RPl+R2qfe3y/LSUkGGEEAKYCUxr5fSVwLvHUb7L6YD2XQi8J6X0AoVCiJ3AWGBZuwl9FBxH+24D/hpuB1LKI25I7Ao6oH0nyvPrEXRA+9rl+Z30JqB9OA2oklLuaOXcFRz5AbVWvo8QYo0QYpEQ4rT2EvQYae/2pQEl+5wvDR/rKo61fTnAaUKIvPBzGrPPuRPh+R2qfSfK8wO4QwixXgjxqtg/rPyJ8Pyg9fa1y/M7KWYAQojvgORWTj0kpdyzA7lVLSyEGAe4pJQbj3CbA8tXABlSyjohxCjgMyHEYCll89G34PB0UftEK9d0iE9xB7dPD8QA44ExwAdCiCxOnOd3qPadKM/vBeAxQrI/RsjMchMnzvM7VPva5fmdFApASjn9cOeFEHrgEmBUK6d/xpHNIweVD0/N9ky784UQBYRGY6uOSvg20BXtIzTi2DcCbC9CO8LbnQ5uXynwiQwZVlcIIVQgXkpZw4nx/FptHyfI85NSVu1Tz8vAV+HjJ8T/v0O1j/Z6fl29MNIdXoQC1C1q5bgS/qGzjrY8kEB4UQbIAsqA2BOofYPZfxFqF120iHg87QN+Afwp/DmH0LRanCjP7zDtO1GeX8o+n+8lZBc/Yf7/HaZ97fL8tDWAEIfSwpOAUinlrv9v745REIiBMAq/uYoH8Sq2gpXX8AxiLeIBvMYu6jG8gYxFmi22UEy1874yEMgwhJ9ASKaDEXGMiOmrfXPz18AYEQNwBbaZ+eq45l90ry8zH8AFeAI3YJeZ766r/t4/9Z2AVUTcgTOwybbDltK/2foW1L9DtG9pR9qNp/1k7hL6N1tfr/75FIQkFeUJQJKKMgAkqSgDQJKKMgAkqSgDQJKKMgAkqSgDQJKK+gCBRwcLdRXCwAAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#now visualize the TRACT receivers\n",
    "for tt in range(len(tractReceivers)):\n",
    "    t = tractReceivers[tt]\n",
    "    if notPoly[t]==1:\n",
    "        for geom in tractGeom[t].geoms:\n",
    "            x,y = geom.exterior.xy\n",
    "            plt.plot(x,y)\n",
    "    else:\n",
    "        x,y = tractGeom[t].exterior.xy\n",
    "        plt.plot(x,y)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "id": "c17c1582-29d1-41bc-8f56-b32ac70747ee",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "I have finished slicing  201388.0 people (VAP of 159890.0 ) and  89090.0 voters out.\n"
     ]
    }
   ],
   "source": [
    "#now, cut and redistribute\n",
    "cutPop = 0.\n",
    "cutVAP = 0.\n",
    "cutHisp = 0.\n",
    "cutBlack = 0.\n",
    "cutTrump = 0.\n",
    "cutBiden = 0.\n",
    "\n",
    "for ct in range(len(cutTractList)):\n",
    "    t = cutTractList[ct]\n",
    "    cutPop += tractPop[t]  #sum up the cut pops\n",
    "    cutVAP += tractVAP[t]\n",
    "    cutHisp += tractHisp[t]\n",
    "    cutBlack += tractBlack[t]\n",
    "    # Now we zero out the pops in the cut tracts\n",
    "    tractPop[t] = 0\n",
    "    tractVAP[t] = 0\n",
    "    tractHisp[t] = 0\n",
    "    tractBlack[t] = 0\n",
    "# Now distribute the pops among the receivers\n",
    "ntR = len(tractReceivers)\n",
    "NTR = float(ntR)\n",
    "for rt in range(ntR) :\n",
    "    t = tractReceivers[rt]\n",
    "    tractPop[t] += cutPop/NTR\n",
    "    tractVAP[t] += cutVAP/NTR\n",
    "    tractHisp[t] += cutHisp/NTR\n",
    "    tractBlack[t] += cutBlack/NTR\n",
    "\n",
    "#             now do the same for the precincts\n",
    "for cp in range(len(cutPrecinctList)):\n",
    "    p = cutPrecinctList[cp]\n",
    "    cutTrump += vtdTrump[p]\n",
    "    cutBiden += vtdBiden[p]\n",
    "        # Now we zero out the pops in the cut precincts\n",
    "    vtdTrump[p] = 0\n",
    "    vtdBiden[p] = 0\n",
    "\n",
    "# Now distribute the pops among the receivers\n",
    "npR = len(precinctReceivers)\n",
    "NPR = float(npR)\n",
    "for rp in range(npR) :\n",
    "    p = precinctReceivers[rp]\n",
    "    vtdTrump[p] += cutTrump/NPR\n",
    "    vtdBiden[p] += cutBiden/NPR\n",
    "print(\"I have finished slicing \",cutPop,\"people (VAP of\",cutVAP,\") and \",cutBiden+cutTrump,\"voters out.\" )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "id": "55419803-eaaf-4181-8848-d8fdad0205b9",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Now, repeat for southern central tip\n",
    "x,y = tractMAP.exterior.xy\n",
    "plt.plot(x,y)\n",
    "pt1 = Point(-76.2,37.8)\n",
    "pt2 = Point(-76.4,38.6)\n",
    "pt3 = Point(-77.4,38.62)\n",
    "pt4 = Point(-78, 37.8)\n",
    "clipPoly = Polygon([pt1,pt2,pt3,pt4])  #LEFTOVER FROM INDIANA. plan to clip off SW corner near Evansville\n",
    "xc,yc = clipPoly.exterior.xy\n",
    "plt.plot(xc,yc)\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "id": "2a56b3e5-803d-425d-8bb6-abfba9aa9716",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "I have flagged  142 tracts w pop 263227.0  to reassign to tracts...\n",
      "[19, 20, 22, 23, 29, 30, 344, 345, 458, 648, 651, 652, 688, 689, 690, 709, 710, 712, 713, 715, 717, 718, 732, 764, 767, 885, 887, 941, 942, 952, 964, 965, 986, 990, 992, 993, 994, 996, 1000, 1446, 1447, 1448, 1495, 1496, 2093, 2156, 2777, 2779, 2872, 2873, 2874, 2895, 3144, 3246, 3287, 3288, 3438, 3441, 4029]\n",
      "I have flagged  75 precincts to reassign to precincts...\n",
      "[297, 300, 340, 347, 348, 349, 368, 433, 436, 481, 526, 546, 757, 759, 760, 762, 773, 775, 776, 777, 778, 783, 784, 786, 788, 1669, 1671, 1672, 1676, 1677, 1678, 1680, 1689, 1700]\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#find all tracts and precincts centered in this clipPoly, flag for cutting\n",
    "# and, ID all nearby tracts and precincts to receive their pops and voters\n",
    "cutTractList = [-99999]\n",
    "cutPrecinctList = [-99999]\n",
    "tractReceivers = [-88888]\n",
    "precinctReceivers = [-88888]\n",
    "popToCut = 0.\n",
    "for t in range(nTracts):\n",
    "    x = tractGeom[t].centroid.x\n",
    "    y = tractGeom[t].centroid.y\n",
    "    if tractGeom[t].intersects(clipPoly) :\n",
    "        CP = Point(x,y)\n",
    "        if CP.intersects(clipPoly):\n",
    "            isSkippedTract[t] = 1\n",
    "            popToCut += tractPop[t]\n",
    "            if cutTractList == [-99999]:\n",
    "                cutTractList = [t]\n",
    "            else:\n",
    "                cutTractList.append(t)\n",
    "            x,y = tractGeom[t].exterior.xy\n",
    "            plt.plot(x,y)\n",
    "    else:\n",
    "        if tractGeom[t].distance(clipPoly) < 0.1 and tractGeom[t].centroid.x < -76.5:\n",
    "            if tractReceivers == [-88888]:\n",
    "                tractReceivers = [t]\n",
    "            else:\n",
    "                tractReceivers.append(t)\n",
    "xp,yp = clipPoly.exterior.xy\n",
    "plt.plot(xp,yp)\n",
    "\n",
    "for p in range(nPrecincts):\n",
    "    x = vtdGeom[p].centroid.x\n",
    "    y = vtdGeom[p].centroid.y\n",
    "    if vtdGeom[p].intersects(clipPoly) :\n",
    "        CP = Point(x,y)\n",
    "        if CP.intersects(clipPoly):\n",
    "            isSkippedPrecinct[p] = 1\n",
    "            if cutPrecinctList == [-99999]:\n",
    "                cutPrecinctList = [p]\n",
    "            else:\n",
    "                cutPrecinctList.append(p)\n",
    "    else:\n",
    "        if vtdGeom[p].distance(clipPoly) < 0.1 and vtdGeom[p].centroid.x < -76.5:\n",
    "            if precinctReceivers == [-88888]:\n",
    "                precinctReceivers = [p]\n",
    "            else:\n",
    "                precinctReceivers.append(p)\n",
    "print(\"I have flagged \",len(cutTractList),\"tracts w pop\",popToCut,\" to reassign to tracts...\")\n",
    "print(tractReceivers)\n",
    "print(\"I have flagged \",len(cutPrecinctList),\"precincts to reassign to precincts...\")\n",
    "print(precinctReceivers)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "id": "264c0a29-0366-433f-ae45-63b986566b34",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#clipPoly block 2 of 4\n",
    "#visualize the to-be-cut precincts, just to confirm there are really xx precincts up there\n",
    "for p in range(nPrecincts):\n",
    "    if isSkippedPrecinct[p] ==1:\n",
    "        if notPolyVTD[p]==1:\n",
    "            for geom in vtdGeom[p].geoms:\n",
    "                x,y = geom.exterior.xy\n",
    "                plt.plot(x,y)\n",
    "        else:\n",
    "            x,y = vtdGeom[p].exterior.xy\n",
    "            plt.plot(x,y)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "id": "34f94200-4cc7-4226-9a6c-d6ae75b3380f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#clipPoly block 3 of 4\n",
    "#now visualize the receivers\n",
    "for pp in range(len(precinctReceivers)):\n",
    "    p = precinctReceivers[pp]\n",
    "    if notPolyVTD[p]==1:\n",
    "        for geom in vtdGeom[p].geoms:\n",
    "            x,y = geom.exterior.xy\n",
    "            plt.plot(x,y)\n",
    "    else:\n",
    "        x,y = vtdGeom[p].exterior.xy\n",
    "        plt.plot(x,y)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "id": "9301c018-ac66-4a64-8efe-a839a349e53f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#now visualize the TRACT receivers\n",
    "for tt in range(len(tractReceivers)):\n",
    "    t = tractReceivers[tt]\n",
    "    if notPoly[t]==1:\n",
    "        for geom in tractGeom[t].geoms:\n",
    "            x,y = geom.exterior.xy\n",
    "            plt.plot(x,y)\n",
    "    else:\n",
    "        x,y = tractGeom[t].exterior.xy\n",
    "        plt.plot(x,y)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "id": "e8d4b939-2d62-4d7a-a866-7283b3e8e383",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "I have finished slicing  263227.0 people (VAP of 201461.0 ) and  133760.0 voters out.\n"
     ]
    }
   ],
   "source": [
    "#now, cut and redistribute\n",
    "cutPop = 0.\n",
    "cutVAP = 0.\n",
    "cutHisp = 0.\n",
    "cutBlack = 0.\n",
    "cutTrump = 0.\n",
    "cutBiden = 0.\n",
    "\n",
    "for ct in range(len(cutTractList)):\n",
    "    t = cutTractList[ct]\n",
    "    cutPop += tractPop[t]  #sum up the cut pops\n",
    "    cutVAP += tractVAP[t]\n",
    "    cutHisp += tractHisp[t]\n",
    "    cutBlack += tractBlack[t]\n",
    "    # Now we zero out the pops in the cut tracts\n",
    "    tractPop[t] = 0\n",
    "    tractVAP[t] = 0\n",
    "    tractHisp[t] = 0\n",
    "    tractBlack[t] = 0\n",
    "# Now distribute the pops among the receivers\n",
    "ntR = len(tractReceivers)\n",
    "NTR = float(ntR)\n",
    "for rt in range(ntR) :\n",
    "    t = tractReceivers[rt]\n",
    "    tractPop[t] += cutPop/NTR\n",
    "    tractVAP[t] += cutVAP/NTR\n",
    "    tractHisp[t] += cutHisp/NTR\n",
    "    tractBlack[t] += cutBlack/NTR\n",
    "\n",
    "#             now do the same for the precincts\n",
    "for cp in range(len(cutPrecinctList)):\n",
    "    p = cutPrecinctList[cp]\n",
    "    cutTrump += vtdTrump[p]\n",
    "    cutBiden += vtdBiden[p]\n",
    "        # Now we zero out the pops in the cut precincts\n",
    "    vtdTrump[p] = 0\n",
    "    vtdBiden[p] = 0\n",
    "\n",
    "# Now distribute the pops among the receivers\n",
    "npR = len(precinctReceivers)\n",
    "NPR = float(npR)\n",
    "for rp in range(npR) :\n",
    "    p = precinctReceivers[rp]\n",
    "    vtdTrump[p] += cutTrump/NPR\n",
    "    vtdBiden[p] += cutBiden/NPR\n",
    "print(\"I have finished slicing \",cutPop,\"people (VAP of\",cutVAP,\") and \",cutBiden+cutTrump,\"voters out.\" )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "id": "84c2cf09-b3ea-4fc2-a55b-62985882836b",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "working on tract 200\n",
      "working on tract 400\n",
      "working on tract 600\n",
      "working on tract 800\n",
      "working on tract 1000\n",
      "working on tract 1200\n",
      "working on tract 1400\n",
      "working on tract 1600\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#rebuild tractMAP, excluding skipped / sliced tracts.  #NOT NEEDED FOR OHIO -- NO SLICED populous TRACTS\n",
    "counter = -1 #Watchout - low-number tracts may have been skipped\n",
    "found = \"no\"\n",
    "while found == \"no\":\n",
    "    counter +=1\n",
    "    if isSkippedTract[counter]==0:\n",
    "        starter = counter\n",
    "        found = \"yes\"\n",
    "\n",
    "tractMAP = tractGeom[starter]\n",
    "for t in range(starter+1,nTracts):\n",
    "    if t%200 == 0:\n",
    "        print(\"working on tract\",t)\n",
    "    if isSkippedTract[t] == 0:\n",
    "        tractMAP = tractMAP.union(tractGeom[t])\n",
    "\n",
    "if type(tractMAP) != type(tractGeom[1]):\n",
    "    for geom in tractMAP.geoms:\n",
    "        x,y = geom.exterior.xy\n",
    "        plt.plot(x,y)\n",
    "else:\n",
    "    x,y = tractMAP.exterior.xy\n",
    "    plt.plot(x,y)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "d4538dfc-7d35-46b4-95c1-c5d93169e971",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "id": "650cf37d-e11a-4ed3-861b-3f3edc411422",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#What is our distribution of precinct's Trump votes by precinct Biden votes?\n",
    "fig, ax = plt.subplots()\n",
    "ax.set(xlabel=\"Biden votes in this precinct\", ylabel=\"Trump precinct votes\")\n",
    "plt.plot(vtdBiden, vtdTrump, marker='.',linestyle=\"none\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "id": "d40408f3-31eb-4b8a-ad91-4e2eb5f422df",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0 0.594475138121547 905\n",
      "100 0.8119777158774373 718\n",
      "200 0.6992592592592592 675\n",
      "300 0.5993485342019544 307\n",
      "400 0.6875 272\n",
      "500 0.7264957264957265 351\n",
      "600 0.7773584905660378 1060\n",
      "700 0.7677642980935875 577\n",
      "800 0.8325123152709359 1015\n",
      "900 0.0 0\n",
      "1000 0.5636856368563685 369\n",
      "1100 0.0 0\n",
      "1200 0.0 0\n",
      "1300 0.4600389863547758 513\n",
      "1400 0.5514592933947773 651\n",
      "1500 0.6760180995475114 1105\n",
      "1600 0.8092485549132948 173\n",
      "1700 0.5227703984819735 1054\n",
      "1800 0.7523680649526387 739\n",
      "1900 0.6794520547945205 365\n",
      "2000 0.7638036809815951 652\n",
      "2100 0.65527950310559 644\n",
      "2200 0.6474622770919067 729\n",
      "2300 0.0 0\n",
      "2400 0.41846153846153844 650\n",
      "2500 0.5625704622322435 887\n",
      "2600 0.7395833333333334 768\n",
      "2700 0.33746130030959753 323\n",
      "2800 0.18006430868167203 311\n",
      "2900 0.754257907542579 411\n",
      "3000 0.41935483870967744 868\n",
      "3100 0.12658227848101267 237\n",
      "3200 0.27671232876712326 730\n",
      "3300 0.28313253012048195 498\n",
      "3400 0.17987152034261242 467\n",
      "3500 0.3192307692307692 780\n",
      "3600 0.7130730050933786 589\n",
      "3700 0.15 640\n",
      "3800 0.46551724137931033 812\n",
      "3900 0.041401273885350316 314\n",
      "4000 0.23880597014925373 469\n",
      "4100 0.0 0\n",
      "4200 0.34051724137931033 928\n",
      "4300 0.7442922374429224 657\n",
      "4400 0.8354755784061697 389\n",
      "4500 0.5859938208032955 971\n",
      "4600 0.7779187817258884 788\n",
      "4700 0.7493333333333333 750\n",
      "4800 0.5265486725663717 904\n",
      "4900 0.608955223880597 335\n",
      "5000 0.5248756218905473 804\n",
      "5100 0.6779279279279279 444\n"
     ]
    },
    {
     "data": {
      "image/png": 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YE0gp93v/HxZCvAhMBTooeouO5A3vC1uqmSYqOJOt2NkHig1KS3EwBNVmTHhS0xTm/HEejTWNLYp4LsZiHf4GTWXeRP57Tb8W5fzmojfR3BqVayspHF/YVtm3e5jsgL28HOOWsXcoSSDmzmH1y43slg4UBb51oZOye0MIiiaBLzUg0XRjRKqok8SCjQrBdoALFhj/29UFiivd/K6mcN1IKe8G7gbwWvR3BKvkhRDZgCKlrPe+vhj4VdjS9jB8v/u56ZspVA6BmgZ/+hOUlGCvqKBMLsfJYByyGvv4C6FkurGDV2leml/KfeklbQwaX/ByrXeqfpf57r6HKcCN6iz3tNl/xIBGPsqc3rLJ7Hvt2IN9rpLdEo2WGyNSRW0mCzbRtL+nysq63ydWdPO7msJ10xlCiKuA/wMKgNeFEJullJcIIQYBT0gp5wFFwIveC7EBz0op3+z0oBZt8C0Kozz/L6hY02oxVlTAvfdi9+zBLneBprbePBUVRuErt5vx6ems/+N7vFbTcRJMZznwAQlwozpKb2qz/4wyB6vKwjRse5Il2hWRKup4BEmTZeRlpnuqm9/V57qJ6SJQUkrT/U2ePFlaSPm39/fIoT95TR6tb2r98IMPpOzVS0pFMcpjKIrx/oMPjO+//W3jc9/ft7/d6fErP6iUa369RlZ+UNm1IL5zqmqbcwW9f3d0cvweyQcfSPnrX5uzDZLpd4qVrOH+Pl3s9+z6vXLoT16T++saIxIN+Fh2olOtmbEmRvosev8UVp+louugKHDhhR1LpAZJtxN2fHRiKQa9f5jH75GYuX6Mmazk7gj3nupqxBKJi7GL31X1Pt8Jz6O3SAy+373NVIX2w8D2Sr6szCh+09xsTJmNlm8y1grIzAouAkKq5Gl2ki0GEOo91Z0ij1FHJ1pcNxEfqlMsRW9ifFH4NpPSAlkq7a2Q8vLoW8dm9c2aVS7iuFhKvNogFUde/m3XnSL3r0OvKG2LR0WA2UogWCSIDgszta9gGMgKieZDaNasGLPK5SUui6XEsg0CdSDh3Ftm7Yzbt93ixV2PWEpKjG1uu83oDBYtgvHjo1DUzPhvhpmxFgkgoEXfnnj4Tc3qmzWrXF5CymwKl1i1QbQ6EDN3xu3brqam+xFLTY1heke42Ig/PoveUvQ9FN/v3mLRB7KM4uE3Natv1qxyeemsumdULdwotEHAOEJnHUiosofbEcVjFFBaaqyjq+vGf//ZyF3tE+V7rsVHH/GROsdS9CZGb1H0omsXTaz9pmb1zZpVLj8CLkISTQs3wjaoqqhixQV/Q3PrqOkKZe/9tyFvIIUWjuzhKMZ4jgJ81pSmta7c1dW5fO6bF16Aq6+OqlyWj76H0mYo15VlFI+MFTNkxUTLZ5xIYuFqiaANnCvWoLk8SBQ0VzPOFWuwl9wYuANpX1MjGNnD6Yji5ZIrLzfOIaWxMMxjjxkrdvl3LO3vuYqK1gJSa9dGyUcf+wrAlqJPAoTA9G6KmGNmX28omOx3dOBERUNDoqLjwNn6ZfsOJFzZQ+2I4tVGvvM0NbVOMXS7Dcu+vLx9VcDWDitmnZDlo7dIAjdFTDF54DVoTPY72stmUbb0ZpzNg3GkVWMve6rzjWMle0WF/0r38Wsj73mqHv47zle34pC7Gawc5NPHN6DpH9FP1OKQLoT0C7zGwkcf8RG6x1L0yUSyuSmiicks4Ygw0+9YUoK9/CmjMmkwSjUWqbulpcbvCrBsGbz3XtzaqIohrHirCE3mo4qZ9NcOc0gvQCIolAf5Jk9iUwS6YuNo/liKuuuEIggiWz56i64xa55yNDGZJZxSJLLjKS83ZnH7iPNorWWugw4eITgoiwCoYjBrmU4VxVzIezg1BwcWbWMOdhprPDhKb+o4JyJM92I8FumzFH2ykyq+62AwkyXcE4mFQVFaapTq8Fn0cR6tZeVnIRTvymsSDlHAR5zNJ5yJjsJqZnBYFhlLbp7y8Pp3XwcIPNM5QveilV5p0Tmp4ruONz1hFBRNYmVQ+Ep2tPfRx4GqiireXPQmukdvUfKP8y08qBiec4FEslc4WtZWlpqhjgPOdA7TvWiVQLDonmTyXZtFuZp5FGSWNmpPLA2KBI3UfG4bnym9k9PQUAEF40MdG818h79QKYo5LvsYGwpQFYkjv6HtAcN0L/o8N9bM2B5Ol79/sviuzaRcw1Fa8VDAZmqj9iSTQeGlu8qhLW4bKUGHYm+qqcereg17HgbIg/RVT7BWnk+ROMxYuY1h2m7six6C8e1+ozA6LVNVrxRCqMDHQLWU8lIhxDXAvcBYYKqU8uNO9psD/AFQMVaeejBiqXsIQQdpksF3bSYXU6hKK14K2Ext1B5/gyI/3/jv+9xE+C9Y71sTOZA/feOSjay8bSW6piMUQdqk09izdSD5+jGOiCKkLpGoaOisVmdx66NnYavJZ3zla+Q9vhZ0DdxqlH4jr+vGJHn0PwC2A7ne958BXwMe62wHb+fwKHARsA/4SAjxipTy8/DE7VnEw3cXN8xkEYY6CoqXAjZTGwXCd80mHXX4l4UWwgiwSr1j5dCqiipW3r7S8M1j+N3dW75glNpI8dkFZI3qxV+fyQUkArD/6BqKFo6nCKDCBsv/Lza1bhJt0QshhgDzgfuBHxlCye3e77radSrwlZRyt3fb54ArAEvRB0E8fHdxw2wuplBGQfFSwGZro0CYeNThXxZaKhJFVRhCFcPEXkbnT2iznS+o2oKEwZ5K+KiS9zfNQBGl6FKgK2lU5Y1v3S4Gv5ES9NA9fIK16BcDdwG9Qzz+YKDK7/0+4JxAGwohFgILAYqLi0M8TWoSj6p2LcTDB50MLqZAxFMBt28jswVnTTzqaF8W+urvDWLUI/ejaB7EonUt/nRHqQM1Q0VzaSBAKKIl8wYJQ/U9pNlm4NHVwJcY5fvYFMFYIcSlwGEp5UYhRGmIxw/UVQW8GinlEmAJwJQpU1LAhI0c4fPdxdqi78oHbTZFkygS0UmZMThr4lFHh7LQ5U+D7vH601tHH+23A9iyYgubl21G9+g40g/w3OKjbK8pisslmsV1Mw24XAgxD8gEcoUQT0spbwpi332Af7h7CLA/dDF7Jq0rz8T4RF3VHjeboulJmNVNYuKRWduy0KWdjj7al4+2l9iZWDbRL0uniCvjJLMp6tFLKe8G7jYEEqXAHUEqeYCPgFFCiGFANXAdcENYkvZARLx8N50Nx82qaHoKpaWgqsbCGKpqKjdJVOlk1BjxYDLE0UeHtQPihO85N2UevRDiKuD/gALgdSHEZinlJUKIQRhplPOklB4hxO3AWxjplUullNuiInkPQGmx6GOs6Tt7IOLtj7XcRB3xdfbxKIiSCDoZNUZtMBmLImxRvkdbs+tMouillOVAuff1i8CLAbbZD8zze78SWBmJkD2VeKwl2UKgByKe/lgzuInM1tGUlxsLYvgWxkjGEVV3beo/amxqMkohlJSYczAZo3tU9T7nmh7xoTrFmhlrYkxhxMXLH5voJ9sMHU17Yj2iinXHFkyb+txTvpWeli2DsjJKS0vMl9wTo3tUUYz/sTTolJgd2cIiFHxKTVUT82QHeogTjW9Edd990e94fEr4F78w/ldURO/YPoJp05ISuPXWVqvGO3KJ5aWHTbD3aEWFsexikG3aMnKPYdaFZdGbmFSYJxU0iU7bM2t+eKxGVPEYQQXbpmVlxlqt7bYzXXJPMPdoGCND1RuM08zio7dIDBEtHmw2v3NXdPdkx/JaEt3RxJt4dGzBtmks2j5W90p392gEHahv3kwssBS9iYk4Cm9Gv3O4xONawjQhu6uSaEri1bEF26bRNN8Ted+H0YH6XDZK7PS8peiTgbB//0QHOKOJCa+logL+s6IKfdkKpCdwlURTYzrfSJRI5L0SRgfqc81HNHLvBkvRpzJm9TuHg8muxWc0nt3kpFRqKHSskmiRIIK9VxLl3mmHb+RuWfQ9lIhDM6nkdzbZtfiMxt3SwXRUFGFY9L7aKUlDMsVwgiVGQdNY4bPolRhqekvRJwERjehSaXhuomvxGY0H3HaeU8v45a1OZpQlkY8eTKXsok4Mg6bRRrcs+p5Nj0qvTDLaGo12SpJJwfswkbKLOyZyBbZOlLIs+h5NLNOuUnLoHidMNMAIDxMpu7hjIlegT81bFr1FbEjloXtPJ5gOvL2yA2NGZ0/p9BPZU/v9PrLvcCC2K01Zit7ExNxz05OH7qlMKB24T9lF2Okn5VyCRNGurfVn3gBiq+itWjfJQKx+/0TXl7GIDeHU7Ymg1s+hJS/x1Yxv8OXPV7Bi9gqqKqq636kn066t9a2fAbEtYmgpehMT8yUETVk5yiJiwunAw+30Kyro/91rmel5l5v1vzHQ5cRZ7mzzfSgFvnoE7dpaP2McYLlueiweb4JtmhrDrj7pI4oWHQgn0BhmcLJuxUvkas0oSCQaDtG6DmuPjQF1Fx9p19YyxwEfbWopVxwLglb0QggV+BiollJeKoToB/wDcABO4OtSytoA+zmBekADPFLKKZGL3TPweFciSFO9d4CVIdMzCed3D6cDD2MfJw7OQEWioaOSftklrT76nhgDCrZz82tr/dMDgHks+h8A24Fc7/ufAquklA8KIX7qff+TTva9QEp5NHwxeyZuzbDobYpIaevICuR1gcl/9/yyS3lm6WbszbuoShvB7Luubf2yJ6ZvhtG5mWbClBBiCDAfuB/4kffjK4BS7+vlGEsMdqboLcLAo+mkqcIodpSi1lFVRRUrZq9Ac5ugKJiJRky+zi9rw2oam87GIXeDS8F57yoc9w4Jro3icD32Ejuzy3+Os9zJ7PYdtYly1eNGONUrvYreDEXNFgN3Ab39PiuSUh4AkFIeEEIUdrKvBN4WQkjgMSnlkkAbCSEWAgsBiouLgxQrtWnW9Fa3TYpaR85yJ5pbQ2oJLgpmIsvZ1/l5XB7QQVCKwgzQQX9XQ127ovsOMY7XYy+xdy5LT4sBhdG5+XIuEppeKYS4FDgspdwY5jmmSSnPAuYCtwkhZgTaSEq5REo5RUo5paCgIMxTpRbNmjTcNpCyGTI1+aNZJ6azT7G3KQpWVVHF2gfWxi9Vz0RLCdaseI1zm94jV68DQKKgYUMTNqROS4fYQqDMFhNdj6mJRVZQSQncfXfQz6hZXDfTgMuFEPOATCBXCPE0cEgIMdBrzQ8EDgfaWUq53/v/sBDiRWAqsCY64qc2zZpOus2vL04x66iiAm5YVIRbL8SmzuC5xUcBN6995zU2L92Mrunxc+eYZcRUUcGEZT8C6WI4X/EUC0BRUdIUkLS0SbeZLYm6nnDcRYlymZlkFKfHwaLvVtFLKe8G7gYQQpQCd0gpbxJC/AZYADzo/f9y+32FENmAIqWs976+GPhV1KRPcdq4blKQ8nIocFVRrDupxEHFJ73ZtugJPE2elmnBHlec3Dlm8SeXl6N4mgHJUFHFNVe4OTp1Xoti7xC07ix2k4jrCUdxJlLZmiTu1eqjj905IsmjfxD4pxDiG0AlcA2AEGIQ8ISUch5QBLzoDTLYgGellG9GJnLPoVmT2GKZQ59gJuVXcbO+AhUNTVc59NIEslyGkpcYf5qu0pDvaN0p1uvGJnrE5GeJK+npjL3r8jYydejwurLc43094SjOOCvbNhleJhnFtS48kvhgLABSynKM7BqklDXA7ADb7AfmeV/vBiZGKmRPJdUt+pwaJ2lC80ajNFwHazlEAUUcQUPlEyaxVZlIdo2duWCaoXZMCdUSN8tIBMJzF8VR2QbM8DJB25nCdWOROJo1nfQUVvSOUge6UEDqCCTD2INEoffYQfx51xycmr3ts2+SoXbMCdUSN8NIxCdHOIpzwQLjf1lZTK8jYIbX3dMT3nZmCcZaJIhUd93YS+yMvuMy3np4C8PYgwJIdEbPHMhTT9o76guTDLUBU+Xcm4pQOp32I7SyspiK5ih1oKarLRa9WZZ9tBYH7+GkuusG4MaHDM/ezt9Wgq5hy1CZWDYReyB9EambojvlHKzy7gkupHgQ5xGavcRO2aoy083CliYPxlrEmJ6g6MFQ9lVX9ovtA9idcvZ+X+UqwKm8g+PRO7EvnBv4WLFWUIlMNwznvOHul4ARWsDJXQkenem6yYKxFvGlWZNkpoWo6JPUpdDl7EofkVjS3Snn8nKqXAWs0G9G01XU2zdQNn5cYJliqaASNVoI97yRyBvrEVqwx0jw6Kw1GBu7c1iK3sR4NJ20zBB+IhPctDElEku6O+VcWopTeQdNV42ZqBqd5+/HMtMlUQHncM8bqbxB+vRbav/kZ9FY08jo/BqKFt0Q+b1uggB/y9LglkXfM3FrElsoRapNcNPGlEgs6e6Uc0kJjkfvRL19A5oGaoat62BdrDJdgr3GaI/cwm3bGI1u/PPdgba1fxSBR1lHoe5G6BHe6yYI8Ls9RjnyWGbYWYrexBglEELo5U1w08aUSC3pbpSzfeFcysaPS2ywLphrjMXILdy29d8vP7+1pk4E8rTPd5+4YCKaWwNDHyJ1yW59KNNVGzZFgqIY5w4HE8xDaHR7EILQ3bQhYCl6E+MJNRhrgps25sQ4ZzyoWEGs6e4aYzVyC7dtfftEqfNpn+8OoKarLRa9jmAPDu7m1zwkfoKiabBoEYwfH778CXxWTro0stNtluump9IcqusG2t60SRqYtegGM47cotj5tM93n1g2kYllE3GWO1m9IYv/vNzIbumgTH8ao16GHv45TfCMNLo9ZKWrMT2HpehNjDtU140/wQ7vTXCjm4ZkaQszjtyi2Pl0lu9uL7Fjq4Bfv2Wc5n21FCnSwRPmOU2SvHDSrZGdEVtVbCl6ExOy68afYCwsk9zopiDZ2iLB7oYOfWInnU+4y0R25kJre5oSVCLo8EySvNDosiz6Hk1YrhsfwVhYsbjRk8Uqbk+sHvpkbY8uaN8nvrC4ipwarzK/++6W7WK1TGTbPi6CDs8kLrCTbg/Z6ZZF32Px6Hr4tW6CGd5H+0ZPNqvYn87aIhJFHa/28JcRWjNgampi0sH494mnN21kw7dfBylR01UWlC9oUeamWSayM0ziAmt0a/TLTo/pOSxFb3IiisN3N7yP9o0eiVWcaMs3UFtEqqjj4RqoqDDkbW4GVTX+mptB1420w4yMqHcwvj6xwFXFfP11fFN+NLfGlhVbWpS5WYuItcEElT8b3Rr2viax6IUQKvAxUC2lvFQI0Q/4B+AAnMDXpZS1AfabA/wBUDEWJHkwCnJbRIto3ujhjhD8Faqqwq23xrxkbUDat0WkijoeroEVK4zjA3g8hqy+1ab1CLJRumHBAtA21aBskJ1uY9YiYmbDbD76HwDbgVzv+58Cq6SUDwohfup9/xP/Hbydw6PARcA+4CMhxCtSys8jltzCfIQ7QvBXqJoGjz0Gy5cn3vUTqaIOsz3aT/fvdBnBAEihIL31/YWihC53NyMr/z45zTaBm9M+YXBzJQCKKpjIFqjIb9nXFPMSTI5psm6EEEOA+cD9wI+8H18BlHpfL8dYeeon7XadCnzlXWkKIcRz3v0sRR9LEukGCWeE4FOoTU2GNSqlOUo4RMG1VcUQnJyPgyEEo+58AUz/6f5CEUYpWwlqhsqcxXNaOgB7WRksW0aVq5AtYhInZG+KxCGa1BymffM08squDM191o2ryr9PBoW+37qKyayDgweZuPJB7I87Yfn9ie+kk4hGt4deJrHoFwN3Ab39PiuSUh4AkFIeEEIUBthvMFDl934fcE6gEwghFgILAYqLi4MUy6IDyRgQ9SnUFStg2TLDBWGWiUARuLa6yzoJlHroC2D6T/eXeqt7xOPysPK2lUhv8LNsVRmHf7CC13+zvcVj8yWjQQfJZPqUe3BQFZxVHYSrqv0g58qyPEpKLoUHHoBXnQlPVUw2PJpuVKm1JVjRCyEuBQ5LKTcKIUpDPH6gWGJAp56UcgmwBGDKlCmdO/4susYkucEh41OoZWUJz4KIFl1lnXTWCThKHSiqgmaYzB3RQdeNXsAX/PzkyR0tSt6HEILNyzaje/TgUxuDcFV1OsgxSapiUuA34naddTYQ2zo3EJxFPw24XAgxD8gEcoUQTwOHhBADvdb8QOBwgH33QZsR6xBgf6RC9yRC7vGS/YEzQRZEtHCUOlBsCpquodiUNlkn/p2Ap0ljzQonN3r92ZNuncTGxzZ2/+N751hIre2GQhWcdtlp7Hh1R4dOpssJTEG6qgL+RCZJVTQ97UbcTSvfASAzLcEWvZTybuBuAK9Ff4eU8iYhxG+ABcCD3v8vB9j9I2CUEGIYUA1cB9wQFcl7ACKc5ErrgTMXst1/L45SB8Km4tE0dKny/x53cPJMoy7XFiYilE+Qmt7lYT/2TKJf7kSUtM1oLu8IQIH5f54PwM7Xdxp+fW9q48YlG1l5+0qkJlEzOrHyI+loA+0bi3hRolNxI6HdiNv1fgUwlgxb4i36zngQ+KcQ4htAJXANgBBiEEYa5TwppUcIcTvwFkZ65VIp5bZIhbbohhSyiuNGDJSHs9yJrukgQdf0Nq4be4kd5ZYyyh9zskc62KfZ+c53wGYDTbMzRcxjnroSqRv7IzDqd3mPraGyWU7krUfs/Hb+edS+vBak4bI58MkBtizfgq7pKIrCnMVzAFh520p0b+1zzRWHCUyxqLe0ZAncdpuROhqDOQIxwf/62o24m6aeC6uOJ96i90dKWY6RXYOUsgaYHWCb/cA8v/crgZWRCGlhEVOiFMBu7xbpbsLQrDI79zxux+eO96W9A2wQk5l6RSHXTDXSLD9/4XN2v7ObBpnFDsawmYnsw47ikezkNAZmVrScp/qgiqdJMzoGIWmsaTQ6Hb11hCBUEfsJTNGut1RRAbffbgTrAVwu88egAl2f34i7aegZsGqtKXz0FhapTYQB7KqKKras2BIw+Ok/YWgfdp5+oNVwLSmByy6Dl15qPZYQrRmmf37Vzri5dhYuhMLxhVSurSTXdQp02M9gjNQayb7XNnPLo0bKZU3+aO74npvr5CYUNNJsrR2MLcOGx+VBURTm/Wle7PPbA8WL2lvvobR9eTn4B6kVxfwxqEDXd/fdLdfoqjTmmGYkOuvGwiLliWBGb9WK/7BimY7Hrbf4VfyDn/YSO/uw8+sV8MQTxvOeltaqz+bOhVdfbdVf/tkzmgbf/S488ww0Ndm59nvfpGDz28h3N4EOK5mHRPC652Iu+WQb3/nLdL7zHdjdDMspY7hwcvEtrUHXcGephluBskO8CDpat6G0fWmp4a5xuYwZ1H/6kzmt+XaumirVgVMfjEOtxl5a2qY9mwqzAMiwLHoLixgTTgDbOyR3Np2NJksB74MqvMHP/AZ44AGerruUW34/vsXbAIZOe/hhGDAAli5ta6S2R9NgzRrj9YYNRTxw13xsa//CqaYspBTGQuZInDhYsgQef9zoLPZh55DNzoNlrccKZ5ZqxBUo/eNFDzwQ2LoNtu2TIdGgnavm0OJnWSEWoKGjCoU5W9N5c1Fre5727FWAZdFbWMSHUAPY3iG5Q+5GZTqaUFDSVCbdOomBuY04b/sNn2m9WCh/gAeJQDKeTzlGPvuwt7HiQ+HF8n7cvfibfPSkhm0TaJqOzaaw82Aev/tu22PqnSftBI2z3MlAl5Oh+h72uoZFFsDtzHoPpe3NlGgQKIjczlXT+MIbaJ7BSAmaR/L5C5+3mVtR/elBILYLg4Ol6E2PbD8TxiJ2hJL94VVadvcBytTncN76SxxlMwBYMXMpmmc6EkEBh6nEAej0p4bLeI2nlAVU6eEpy8xMuPb2ApqbBaoqufwKhZUr4eWX6TBpSsouXN5BXuvo/BrO1Zej4kHTbRzLnx+W3EByWOTB0lkQuV1nlnX1XNS121os+NOvPp3KtZUt7/PPKISP9qIqsVsvFixFb2piuFawRXtCzbzxU1r20lLs3m3XPrDW66ZRAMkIdrOPYhR0huFEQefa8dt5ZJu9jTunFen9Myw8X3AWDLd0NvU0N2cjEWiaTuXOU2harw5K3lehOKDLO4RrLarZjlR0hC4Rik5RzXbgys7bpTsSYZHHIu++syByu86sqKSEsvGT28Q4CscXtrzfmmODj/YS7vpCwWIpeovIifaDFM7xIpUh1MybTs7XkO+gWdpQ0NCxcc7FfTj1eTWn7VvFIKrRUWnMyOPRR1szBaUEgQ5IVHSmsIEPOY8h7OMy+RozHU72TrqS6XeVsGzRLlTOQPNuO0RWsT19dJsKz2ee2c2aI6Fca2kpIsOwUEUyzrSOVe2nroLI7Tqz9rER//effnoAACXGVp2l6C0iI9oPUjjHi4YMwWZ/VFR0LL7md77NNXaeEmUUSydOHBz8j537fnSMxx6eiZ0qqrDzyDeyuHKhMQt2xQojIHu6+1P6UcMwnOzGwRCq+Dn3cytLUZ0aHPgjyl3/oe4bQ3BveIZK7BRTxbWLZvPT8SH2caFkuiS7uyVWtZ+i1C66dygWY8+NpehTikRMDY/2gxTO8aIhQzAPrq9D8ZVTBnC7qVvxElvLPThKHZSW2rnHZqey2bDYFB1kXj8eeayZN16w86Ors7hyYVHLKX113P6zIh992etIj4ZAQdNsjGAXKhoKEt3lgrIy5t55Jzw2l40vOJl89WzmLrS3HCuka128GF54Aa6+uvudzRQADZVY1n6KQrvoLT43y6K3CIZElSeO9oMUzvGiJUN3D66vQ/E9nEKg29J4aWkdldp7LemHf/qTvc0sfaPfKOLKhV2d1k5VmZHnfrzyOG/8dRt7GIaGikQz1MBXX8H//A9zH3uMuW8tNH7zB54OfX3YigpYtMi4lrVrjaFFsiry7gjV8k5QHR3LorcIjkBWre/zWN60UR7aVzGEmgW/w4Ez+EUz4uVe8O9QvA7xT5lA5eNH2lSJXHi3nfGB3CndKBGf33bLw28zkAPsZyBPcTOX8iqFHG3d8IUXDOU8e7YxeSjU9WGTtZR1uARjeXfjkosVPoteWD76nk3Q2ZXtrdr8/PhZ+FEa2rednJNLWVlwqzJFU4Zuz9GuQ8lf8gaqchjNr0pkQHGCGHG1XH9TMwowmU1MZEtbJQ+Gu8WnrH3J8qGsD5vspayjTScuuaDaMsIRgO/nUy1F33MJ6advr4SS0GrraqEO0+CvwSsqsC+6mjKtAKcyAsfiO1vlDaOmi7PcidbkQUqBjiCXEwxhX9vzq6phzYOhpP0t+mCVdjIEWOPpQgngkguqLaPgLm216EMXOxQsRZ9KtDcjk8xq667ao+nwKgi7XoldVEPNRcDcwAog0IjrgQfaKDJH3RZU2YyGgopOHrUdz6nrHUsHhOqjhy5HQAkv9x7veFMAlxxlZXFxgfn6FsWaMGXRLYGezGSw2trRvtqj6az59nTmAumsYqG/YvYFQ/0Umb38KW5mP3txMBQnmTQh09IRze7Wc6alhVc6IEhMseRwvEej4T4rUXCBWemVFsHR1ZOZhGlx4RTeShidKYjuaroEKu5VUgKDBlHMBorZhwROzbgE5cF7jCDhwYNGFbRgLM0IMIXHLxExhHCelSgYU7513xM+YUoIkQmsATK82z8vpbxHCDER+CuQAziBG6WUJwLs7wTqAQ3wSCmnRE16i/g/mQkf15uMQAqiOwXQmSK76y547TXweBA2G1kP3hP3zrq9aGPzD7H2gZ2tI6x4/P7JNBqN8Pdp8dFHS55OCMaidwGzpJQNQog0YJ0Q4g3g/zDWj10thLgVuBP4RSfHuEBKebST7ywiwftkVrkKcIoROPInYSeCGuJdYYpxfZLQlQIIpMh86X1gROZiXfykE/xFG5t/iG2LnmiJmXxz8RkULboh9r9/DzImmr3rAqclunqlNMonNnjfpnn/JHAahqUP8A7GurCdKXqLaNCJL75q8QusuH0Dmgbqok3MoZA3F73Z8oDOWWysPhSx0jfFuD5FaJe90yG9T9M6X3ov3ABsiKKtfWBnmyyoxhfeiP3v38OMCbd3Dd90MywOLoRQgY3ASOBRKeV6IcRnwOXAyxgLg3emQSTwthBCAo9JKZd0co6FwEKA4uLikC4ilZHQau35Vqlo9wA4a3LQdIHUjQdy05Ob8DR5QILH5WHl7SuRugxv4Qh/2mcnVFYasqXwgxgXgk3v8ynBcCZJhUH7LKisq+fC2qdi6ztPBmMiiiMOl1fRZ8RY0Qd1dCmlJqWcBAwBpgohxgG3ArcJITYCvQF3J7tPk1KeBcz1bj+jk3MskVJOkVJOKSgoCPU6UhIhBBzYbzzcjz0WeOYrrQ+k79c8sPFAy7J2QgikJtvkpoeNb1z/rW8Zyujxxw3ZKirCP2YqUFFhBFjDbQdfB6qqhuL+n/8JrLy7miQVA3xZUBfcdwFlq8ooWnilIdd998XO0vZvCzOmBfs621/8Iir3vtujowiwJdp144+Usk4IUQ7MkVL+FrgYQAgxGgi4IoGUcr/3/2EhxIvAVFpdPhbdUbWvW2vPXmJnzuI5rLx9Jbqnde1SBIy+bDS73toVvdx03+Qfj8fcVlcs8bfoIHJXQ7DBR58SDGeSVJh0yIKKdXDY7IHYKI84XB4t5ssIQnBZNwVAs1fJ9wIuBB4SQhR6lbcC/BwjA6f9vtmAIqWs976+GPhVdC8hxbEPaXWX2Gxwyy0BU+waaxqRumyj5G2ZNqbdNY1pd02LbmC2J0+hb+9DXrCg6wc/2GF+MArUXwnG0EefcMycFhzle9/t0WPun4fgLPqBwHKvn14B/imlfE0I8QMhxG3ebf4NLAMQQgwCnpBSzgOKgBe9BXtswLNSyjejfREpzcBBQVk4/v5UxaYw6ZZJTCyb2KLYo5qbbnarK5a0t+ig8wc/FoFFMyvBnkCU732XWRS9lPJT4MwAn/8B+EOAz/cD87yvdwMTIxezh+O7mXy+2AA3V9xnlaaSwgljrdgW5V1WZvwF2j+RgcUelKIYd6J477s1PeYLg4M1M9a0VB1rpMHlXVQ0SMswqWaVmoUI1ortUHKiPYlycSVrimKcOqeYzDEJE48mzWHRW8QeKSXrvjpK+Y4jNGs6lccaKd9xpHWDZEg5S1bCadtgLbpEubiS8X6JU+fUthR2hOnGUaBZ07HFutANlqJPKFJKKo818pu3dvDapwfIsClk2BR0CTecU8zw/tlcMKYQ8kt7bvAz1sTa6k6EiysZg+Vx6pzMVgq7WZMxnxULlqJPGA0uDxf/fjX7jzcBcP1UO/defkbgVKuCHhz89BGrYX0YVreZhv6dsmCB8T/GRdCiRpw6J0epA9Um0HSJahMJL4XdrOmkqZZFn7KkqYJRRb1bFH3JiP5d59OmUvAzVGI9rA+hbc029O9A+7YqK0u0RMERJzeXnX2UyeU4GYxDVmPnQjqf1B8lujBSPLoe88lSEOTMWIvok2FT+ctNZ7W8H1mQw8qtB6iuO5VAqUxKoGF9GFRVVLH2gbVUVVSFLUqgob+piFJbJYSSEqNuv6/IWySzjTujvBy75mS6XINdc8a+fbqZSdvskZZFn+pkpdu485LT+M1bO5j3x7UAOPKzKL/zggRLZjKiMKyPliVu+lWwumsrr3VZlT8JZ02OOd1PsRzBxTt+0U3soVnXyUmLvRq2FH2Cue2CkZxpz+Pd7YdZ+v4e8rLSEy1S4mk/1I3CsD5aQTjTr4IVoK1aYgr5DdgXXU2Vq4AV+s1oShpqhq1jp5foHPxYBmbjnQnVTcfisYKxPQNNl+gSPj9wHIAZo3t4QbfOrLkIYxTBWOLB6jczz1cwlLoHR+lN2EvsbUcyijQWMteL0VCROh07PTPk4KdSJlQ3HYuVXpnCSCk5XO/i+iUfcqTBRX2Th3RV4XuzRnL7BSMTLV5iLboYWXPdWeIVFcbl+iow//jHkJeXXElOgdxTbUYyEpzKCBzsQtU1NEXp2OmZIQc/1UpsdNGxGFk3lkWfchw83sS5D6xqea8I+N8rx3HFpEH0zkxLoGReEm3RxdCa68oSf/jh1tI1mma8j3G596gTyD3VYSSz+E7sNZsp68xHb5Yc/HhnmSXIuNF0iWJZ9KnFhj3H+PpjrVF3myLYcs/FZGeY6GdItEWXAGtuyRJ4+eWOn/uXe08GRR/IPRV4JDMXO50kFaaaNR0MsTRu2ncg7VYIk55JxEHPW4o+nmzYU9Pyemh+Fm8tmkFmWpRqUUfLIvG36Gy2xKwiFUdrrqICbr+9tdx/e+Jq1Eb4G3bmngo5pmC2ORuxtrZjZdy070AWL4ZFi9qsJyAXLkH0tbJuUoqZowv57ds7Adhb08iYX7zJjecUc/9V4yM7cDQtEp9F51u68PHHYfny5PFfhEh5ufF8+yOE0cd94xtxnFgapd+wjVKPloJMZMwmHq7EWLmr2ncgL7wAbjceCe8PO4vXx5zPwex+KIcORed8XWAp+jhy+qBcHvzaeD7bfxy3R+efH+/jmfWVHDjexMWnF/G1s4aEV8kuFIskmIe2pKRVAyZTYawwKC01/PAulxGE/eEPExSEjbZVGS0FmeiYTTxcibFyV/l1IJ6MTLbNv45X5TBeHj2NIzn96N3UQOnezdz437Ojc74usBR9HFEVwXVTWxc+H1XYm1VfHMJ59CQ//fdW/rjqS351xTguPL0otAMHa5GE8tAGc8xE51tHAdO4pKNtVUZLQSY6ZhOv4HAs3FXem2v5c2t4MHMMp/bbSJt8GaXqcb7Wr4ZZTfvJuHZOXNozmKUEMzHWeM3wbv+8lPIeIcREjOUDcwAncKOU8kSA/edgLFCiYqw89WD0xE9uvjVjON+aMRwpJU+s3cP9K7dzzyvbQlf0wWqrUB7a7o6ZaEsvipjCJR3tHiccBRmo4050Fo5peuLwWX6qL6d62Tj9yB6W3ngmA2YFXF47pgRj0buAWVLKBiFEGrBOCPEG8H/AHVLK1UKIW4E7gV/47+hdfvBR4CJgH/CREOIVKeXnUb2KJEcIwXkj8wHCr3UTjLYK9aHt6piJtvRSkWj2OKEqyK4mqoV6HL+skqgoZ1P0xGFSXs4TL/6Fm//rl3xeMIxz367jwZxKvj7FHpe0Sh/BLCUogQbv2zTvnwROw7D0Ad4B3qKdogemAl95lxRECPEccAVgKfp2+Jc++KSyljOL+0b/JNG0jhJt6VkEJlD5iGDoquMO9ji+zsIvqySpJiLEgtJSht93H+uWfJO1w87ijzfezU//vZV/f1LNI9dOYnBer7iIEVTkTwihCiE2A4eBd6SU64HPgMu9m1xD4LTcwYB/qcB93s8CnWOhEOJjIcTHR44cCbRJSnPweKslf9WfP+DH/9zC0nV72B/tapb+FQIjPc6qVXDffT37QTYT3VRK7BJfx62q4Xfcvs5C1433/hMReiolJbB4MUJVmbFnE//6zU08OCmLzVV1zP5dORv3HouLGEEpeimlJqWcBAwBpgohxgG3ArcJITYCvQF3gF0DjU0CZixLKZdIKadIKacUFPS8ei+Th/bj8bIpjC7KYXRRDqt3HuFXr33OJY+s4dcrt9Po9iRaxI5Eq9NIBWJVVjcUIilRHI2O29dZKF61oijWaA8MF5aug64j3G6u3f0h911xBk3NOs98WBkXEULKupFS1gkhyoE5UsrfAhcDCCFGA4EiDPtoa+kPAfaHJ2rqc9HpRVzkF4jdeaieix9Zw5I1u7nkjAFMHhoDd45F5JglMB2pOy1SX7i/azCaPvpkx/u7uD06L064kL9xJttf2Er/nIw2WXixJJismwKg2avkewEXAg8JIQqllIeFEArwc4wMnPZ8BIwSQgwDqoHrgBuiJ35qc+JUc8vrs4rzEieIRdeYJTBthgyVZA6cxoqSEurffJevv7mf7XovxmRl8fDFw7hi0qCuV5WLIsFY9AOB5d4MGgX4p5TyNSHED4QQt3m3+TewDEAIMQgjjXKelNIjhLgdI1CrAkullNuifxmphZSS3729kz+991XLZ0LEL0JvESJmCkxbitaUHDxtPF+8UQvA2IG5fFJZx+f7T5CRptArTWXMgN5MHtqPgt4ZMTm/kJ0V+UggU6ZMkR9//HGixUgY2w+cYO4f1ra8f/jqCXz9bHPWP09JwpkIlgKTxyxiy3tfHOavq3ex//gpmpp1XM0aLo+Oy2MErxUB3589ikUXjg7r+EKIjVLKKQG/sxS9+Rjxs5VouvG7LL91KjN7+mIk8cQs/naLHsORehe/eu1zXt2yn4n2PF6+bVpYx+lK0VuLg5uQn8w5reX1gqUbOOkyYcZNqtJV5ooZMmssUoaqY438/KWtzHj4PV7dsp9J9jweujrCAoedYNW6MSELZ4xg4YwR/OT5T/nHx1X8fUMl35w+PNFi9Qw687dblr5FlPnFy59RvuMIF44t5IcXjeaMQX1idi5L0ZuYeRMG8o+Pq/jt2ztweXT++zyHuRYpSUU6y1wxS2ZNvLBiDlGhuu4UH+05xkm3h35Z6fTNTqdvVjp9s9O4YWoxa3YeYdv+ExTlZsZUDstHb3J2HKznksVGpYlJ9jxeCtN/ZxEhPcmi70nXGibNms7GvbW8sfUA1XVNeHQdAfTNSkdRBEcbXGzbf4Ij9a6gjvfXmyYzZ9yAiGTqykdvmYcmp7quseX15qq6xAnS0zFDjnq86GmjlyCob2rmk8o6Nu6tZePeWj6prOWkW6NXmoqjfzZpqkBK2HmoASklub3SmD6qPxMG92HqsHzyc9I5dtJN7Uk3xxq9/0820+TRKOqdwawxhTGV31L0JueC0wq5fqqdv2+oIis9PpMrLPwIt0hYLIm1WyWR8wL8rs199jm8/9VR1u85xv66U6SpCgW9MyjKzcCjSWob3cbfyWZqG90cP9VMfZOHU80amTaF7Awbjv7ZTBzSh7LzHORmpnV7Tl97uj066/fUUL7jCB/sqmHHwRPo0kiBHDMgl6+dNYRpI/OZPqogaHdqrN0zXWEpepNTsauGV7ccAOBXV4yzfKfxxIwujHjIlKjRi9+1fT5oFIv+5xF21mukqYJBeb1o9ugcaXDRrBnuZlURhr87K42+WekU98uid2YavdIVmpp16pua2XXkJO9uP8RTH+7ll5eP45IzitpOPmzXnl+8+BbPufrx4ifVHD/VTLpN4WxHX743axRTHH2ZZM+jd2cdhomxFL2JqW9q5qf/3kqvdJU3F01nyBdbzKd4IiXYjisRHdyKFdDUZKwcbhYXRrzcKokYvXiv7Vh6Ntde/UsyGpr4843nMGtMIZlpxmhW1yXHGt2k2xR6Z9iCmjH+6b46fvLCVr799EYmDOnD7DFFjB3YmzSbwsm3P6Fu7Cw+KxzOx0NO56v3TpCuNnDJuAFcPnEQ54/sT68UGElbit4E1DW6+XD3MSqPnWR/XRNH6l0cqXfxaXUdLo/O/VeON5T8vfe21vqOl+KJpYIN1jpNhGVdUWEsju5LVrDZzFGF0UzlFqKN99o2DTmd+oxsLh+awZwzBrRZoENRBP1zQisTMGFIHq/cPo1/fFTFM+srWbxqJ605KEPhou+Q29TA5AM7uWHmaVx11TT6Zqd3dcikw1L0Ceb9r47yrRUf0+jWAMhOVynqk0lBTgbXTrFzzRQ74yo/77igQzwe8kAKFqKn+IO1ThMRHPQtjg4gBNxyS+KteUjNoLC/MbFqFee/t5qLs9J4xulCfXWb4bKMkDRV4aZzh3LTuUOpb2pm95GTaFKSk2EjZ9sWBnz4Kcq1F6ZGewbAUvQJoqlZ47olH7K5qo6+WWn87ZapjBnYO3DAaEV564IOigIXXmhY9/FQdv4KdsUKWL48epZ1sNZpIqzY9ucsK4v9OTsjUEA4VRRSAGMi82c/5TEpufaxD1n75dGon7J3ZhoT7XmtHxSdD7POj/p5zISl6BPEjoP1LemSr9x+PvZ+WZ1v3F7pxEPJBzovRNeyDtY6TYQVaxbL2YwB4WjSyWhNCIGiQL8Uc6EkCkvRJ4BNlbV87c8ftLzvtjRpopRO+/NCW4s+GpZ1sNZpLKzY7uIPZrCcUz2nvYvR2sjCHP69qZpdRxoYUZCTMBFTAWtmbJz5pLKWq/yUPMC7P5rByMLeCZIoRNorx2RN90wWSzlZ5IyETu6hnYfquW7Jh3g0neW3TuXMYmuFta6wyhSbgAaXh399XMUvX/0cgNsuGME5w/Ip7peFo392gqULk2RWQg88YCyirWnGgtj33Wesf+vDTB2YmWSJM1XHGrluyYfk56Tzyu2p7UePlIhKIAghMoE1QIZ3++ellPcIISZhLB+YCXiA70opNwTY3wnUAxrg6UyQVOf3b+9k6ft7APjLjWcxd/zABEsUBZLZrdBVgNdsHZgZXEgJwuXR6J1p48DxpkSLktQE46N3AbOklA1CiDRgnRDiDeBXwC+llG8IIeYBDwOlnRzjAill9MPnScTssYUtiv69HYe5+IwBqH75wXElWhZiMud0dxX3SOYOLMmpPenmpc3VbNxby6a9tew/3kS6qnD/VZGnWPZkulX00vDtNHjfpnn/pPcv1/t5H2B/LARMVnRdUt/kYVNlLeU7DvPshsqW717/9AA/v/T0zmtvxJJoWqtmyUwJl84s5WTuwJKY7QdOcMPjH1Lb2MygPpmcObQv3xral8smDgp5kpRFW4LKuvEuDL4RGAk8KqVcL4RYBLwlhPgtxkpV53WyuwTeFkJI4DEp5ZJOzrEQWAhQXFwc0kWYAU2XPLuhkmXr9nDgeBOnmrWW79JUwfzxAzl7WD+amnUuHFuYGCUP0bdWU9GtkOwdWCIIc5QopWRvTSOrvjjM4nd3kmFTeeMH0xk7MLf7nS2CJihFL6XUgElCiDzgRSHEOAyl/EMp5QtCiK8DTwIXBth9mpRyvxCiEHhHCPGFlHJNgHMsAZaAEYwN73ISx19X7+I3b+1oef/9WSPJybQxblAfzizua556GZa1Ghyp2IHFii5Gibou+bT6OJ9U1rKv9hSNbo1Gt4eTLo2aky6cR09S29gMwDnD+vHQ1ROSNznBxISURy+lrBNClANzgAXAD7xf/Qt4opN99nv/HxZCvAhMxQjuphSnD8plcF4vqutOATB9dAFnO/olWKoAxNta7cEZIz2GTkaJL2+u5jdv7WBfrfFMZKWrZKXbvP9V8nPSuej0Is4s7suUoX0ZVZQkKcZJSDBZNwVAs1fJ98Kw2h/C8MnPBMqBWcCXAfbNBhQpZb339cUYQdyU44LTCrnjktH88B9b+P6skZxl5pzfeFmrZstesQidYDrqAKPEpev28KvXPmf84D786KLRTBvZP6H12Hs6wVj0A4HlXj+9AvxTSvmaEKIO+IMQwgY04fWvCyEGAU9IKecBRRiuHt+5npVSvhn9y0gsp9waj773FUvW7GZwXi++e8HIxGXUmIku4gFVFVU4y504Sh3YS+wJFbNH0o0Cr6qowrliDY6l92DXnAE76ja/od8o8Z0+w7nvqY+ZNaaQx8umWM+CCQgm6+ZT4MwAn68DJgf4fD8wz/t6NzAxcjHNx5F6F1ur6/jqcAPLP9hLdd0prjpzMHfPG9NSO7vH00k8oKqiihWzV6C5NdR0lbJVZYlT9snmWoqGvN2MtDYu2cjr330dqUkUrudMPmGiayv2dh11h9/w7ruRUvLg71czsiCHP994lqXkTYJV6yZEqo418sdVX/LS5uqWlW7GD+7D774+kXOH5ydYOpPRSTzAWe5Ec2tITaK5NZzlzsQo+mRzLUVL3m5GWj4lD6CjspEpbNEnUbIhm1EVVdhL7J3+hpsqa9l15CT3XHa6ZfCYCEvRh8DhE03c8MSHHDrh4vqpxVw+cRBD87Ppn5Me1Eo3PRL/eIDXGh2dP5Y16WqLNegodSRGtmSbGBUtebvIvHKWO5G6f9KbcV97sLH25Voq3lpB2aoyHKUOFJuCpmsoNgVHqYOjDS6+//fNDOqTyZWTBkdwoRbRxlL0XfBZ9XGe37iPAX0yWTh9OH8u38W+2lM8/+3zmDzUxMFWM+KzRl0uilSV7/zwV2zLm5FYH32ypZpGS94uMq8cpQ4UVUH36K3bC0AKkLRY745ShzFDBtA8Oi9XVPLUezuoa2zm+W+fl3IrNCU7lqIHTjQ1c/cLW9myr44Dx5u47mw7N5cM5YbHP+REkweACUP6UNfoRko4Y5A1mSNkystbV8jSdfr9/hdMX7MGEhmITbaJUdGUt13mlS+wmtavFyfyMzmRqdLYJ4Nhc0YyqKGZI09sRnp0hCLIys9iy392s3tYbw4NzqF6WB/qDtcxqk8mf114LuOH9In4Ui2iS4+vXrlm5xHKlnaoxYYQkJuZxtmOvry7/XBLjvyFYwt5YsHZcZEtpaiogJkzodmYHIOiwP/+b9uKkRYJwRdY/ez0fnw0czCeTnzrZ6+p5nifdA7Ze3O8n5EqqTbrFOxvYMQXtXzrmnGU/mxGPEW38COi6pWpyE9f+JTnPqrikWsn8vCbxmxWIeCxmyaz8KmNpKmCb00fzn+f5yBNVXjk3Z0cON7EgvOGUlbiSKzwyUpJCfzpT3DbbYZVn5FhfldJJISbHZOALCBnuZPDeelUzLYzoKqe0duOkXXCTZ8mD1euuIr3mpv5+9o9fDRjMOlNHoqqG5gzqA8T+mXz5U9Wobh11AyVERcMi4u8FqHTIxW95g02/fAfW1o++8uNZ3HxGQPY+b9zUQTYVKXlu2gsTmwBLFwI48cnj6skXMLNjonGYuxhdBT50+x8tmEvCMGM9/Zx7S9n0VjT2BI/KQFu7pvD4/OeQa13Y0tXKfvRDOwldqrOHmLNh0gCeqSi96V9XTFpEIW9M/je7FEtRcbSbUpXu1pESk+oIRNudkyki7GH2MHsOXqS5R84eX7jPhpO68tVGel879/XBVTY9hI731p5Ywelbi+xWwo+CeiRin6SPY+nPtzLjoP1zBhVgK6bL05hkTz8e9M+Fr/7JUfqXQzp24vLBp7Df/UbyKBjB0LLjol0MfYgO5iqY43c88o2/vPFYdJUwaUTBvHN6cM4Y1DXQVRLqScvPVLRXz15CJlpKr9/Zwc//tcWhDAq5/XplUajW+OOi09joj0v0WJ2T7LN6kxRfvHSZzR5dOaOG8CRehe//+IYj9z6V8apjUwb1o/SotFM1SVKd7NEI12MPYj0y81Vddz85HqQ8MMLR3P9OXYKe1s1aFKdHp11o+uSTZW1rNl5hD/+56uWz+ePH8ijN54V8/NHRLLN6uyMFOisblm2gfd2HAEM15+uSzzeUaJNEXh0yajCHO6/ajxTh4VY0TTU9ulm+5ufXM+Og/U8/+3zKM7PCk0WC1NjZd0E4HhjM795+wtqGtxtasX3zUrj7nljEihZkCTbrM5AdNVZmagD8Gg65TuOsPdYI2mq4Jxh+Zw2wCipu+NgPWMG5lJ5rJGq2lO4/SYazR8/kIf+awLvfn6I37y1g2uXVPD8t0uYPDQEZR9qTKOb7Q8eb2Ly0L6Wku9h9BhFL6Xkw93H+HRfHSfdGi9vrmZvTSNFuRnUNjYzbWQ+35w+nNLRBclRziDZZnUGorPOykSjFU2X3PD4ejY4j7X5fMrQvpSeVsAfV32FJiV9eqVhUwRuQFUE5wzrx0/njiEnw8aVZw7m+Klm7nllGzUN7oRch49+2enWQts9kB6j6F/Zsp8fPLe5w+dHG9ys/P70FgstaUi2WZ2B6KyzMtFopVnT+fzACcAoXjdhSB9ye6XxxtYD/PbtnQwvyOa5hee2+Lk1XaIIOhgLf99QybjBuVx0elHcr8Gf0UW9+cfHVUgpk8OgsYgKPUbRD81vXZ6sKDcDm6JQXXcKTZds2FOTfIoeIk5VTHhN+M46KxONVjLTVF787nk8v2kf72w7xDPrK7n6rCH858el7Ks9xcC8TNL85lx0Vpa3V7rKKbeGpktsamIU7N6akzz14V7GDOhtKfkeRjArTGViLP2X4d3+eSnlPUKIScBfgUzAA3xXStmhloAQYg7wB0DFWJDkweiJ3z1rvzzCPz6qoq6xmYn2PARQXXeqZcm/r08Zwg3nDI2nSOETrN86iO1MUxM+UGeVwNHK4RNNVNU2cqLJQ32Th/qmZo41uKlpcHP8lFG+4cziPBRFhOTnvmXaML7/90+4ZPEa5o8fSOmYQiYOyYtrvfb/8yYc/OG6DstLWKQ4wVj0LmCWlLJBCJEGrBNCvIGxJOAvpZRvCCHmAQ8Dpf47elelehS4CNgHfCSEeEVK+Xk0L6IrHnrzCz6rPtHms6+dNZgzBvXh/JH9k8eSD9ZvHeR2pqkJ3xkJmFj1+f4TXPHoupZ1Bvzpl53OtJH9uWbyEGaMLgj52JdPHIQqBMve38Of3vuKP/7nK/plp3PVmYOZPqo/Zxb3pU+vtGhcRkCamjXW76mh9LSC5LnnLaJGMCtMSaDB+zbN+ye9f74yjn0w1pBtz1TgK+9KUwghngOuAOKm6H86ZywPvfkFW6uPA3D91GIe+Nr4eJ0+egTjt66ogEWL4JQxWunKv+0odaCaoSZ8N5xya6TblLhYvuU7D9OsSf5601kU5maSm2mjd2YafXqlRWURjfkTBjJ/wkDqGt2s/fIob3x2gOUfOHly3R4ARhXmMKooh7rGZg6eaOJ4YzNNzRoZaSoFORmMHdibC08vYuboAnpnBu4UNF1S2+jm2Ek3RxtcHDvpZsfBev69qZrqulPcPXdsxNdhkXwE5aP3WuYbgZHAo1LK9UKIRcBbQojfYqwle16AXQcDVX7v9wHnRCRxiJw/qj/9e09g7h/WMmZALr++Kknr1nTmt/a5afLz4fvfN0oB+7DZOvVv20vslK0qM12dkmMn3by17SCfVNayuaqOLw83kK4qjCrK4Ux7X+645LSAlm804g2+KSWzxhTFtBRGXlY6l00cxGUTB9Hg8vBpVR0b99ayqbKWLw7Wk9crjTEDetM3K53MNBWXR+Pg8SbWfnmUlzYb9tT/zBzO3XPHUnvSzc9e3MrOQ/UcO+mm7lQz7afGCAHTRvTnwavHM31U6KMRi+QnKEUvpdSASUKIPIzFvsdhLAb+QynlC0KIrwNPAhe22zWQGRZwhpYQYqH3mBQXFwcnfZAM6ZvFZRMG8cqW/Xy4+xglI8yz5N+uIw38+J9b2Fd7irEDe/PLy89geEFOxw0D+a393TSK0loC2Mctt3Tp/jDTlPaqY408sXY3//i4iqZmnbysNM605zF33EAa3R6+OFjPsxsqqTzWyJKyyWTY1JZO7lD+WFYs2ha1eIMex0mEORk2zhvZn/NG9u92W02XrNl5hFv+9hGPrd5NuqrQ4PLwxmcHmTWmkJIR+fTLziA/O51+2enG/5x0BuRmkpdlLQTSkwkp60ZKWSeEKAfmAAuAH3i/+hfwRIBd9gH+T9wQArt4kFIuAZaAMTM2FLm6Yueheub/cS3NmkQISEtQxkMgDtc38atXP2dzVR1XTBrEy5v3M+t3q3E+OD/wDiUlHJ80hX9+VEXtm19Q/f4uZoycRpNiI0P30K/pBMU1+xlWux81zQZlZfG9oBBpatbYWn2cFRV7ef3T/aiK4MpJg7ll2jDGDuyYGbL8Ayf3vLKN037+Jtvn96XXJReC201/xcZA7Waq9CERxRsc3sys+177nP83fyxZ6eZKSlMVwQVjCtl678X8/KXPWoKrvdJUHr3hrDYT/yws/Akm66YAaPYq+V4YVvtDGAp7JlAOzAK+DLD7R8AoIcQwoBq4DrghOqJ3zvHGZn70z81s2HOM4QXZNGuSwXm9WHbL2Ywu6joQ1eDy8O9N+3AebaRfdhqTh/bj3OH9op6Otmr7Ib6x3Cjz8IPZo/juBSNwHj3Jln3HufnJ9Vx91hDOHZ7PgD6tdUhONDXz7ac2UrG7xvtJX16et6jDsbOlhzMLM7k+28HsZi1hizTXnnSzuaqOzVV1HK5v4thJN7UnmznW6ObEqWZqTrrRdElOho1vTR/OLdOGtbne9iw4z8GhE038uXwX175ZzaOZedhPHUCRMEzZyz5hDyre4NF0tuw7jk0RZGfYyMmwkZWhcvawvgzvn80z6yvJSlf5f/NPj3KLRIfemWn84boz+fbMEWypqmPy0L6Wkrfokm5r3QghJgDLMdIjFeCfUspfCSHOx0ibtAFNGOmVG4UQgzDSKOd5958HLPbuv1RKeX93QoVb62b97ho2VdbR6Pa0WDv+3HPZ6dwyLfDiCFJKXtmyn1+v3M6hEy56pamcatYAOKs4j999fRLD+mcH3LczpJRUHTvF9oMnyLApnO3oR3aG0bd+c/lHvLv9MBefXsRfb5qMogg0XbJ03R4eW7Obow2Gr92Rn0XJiP4cP+Vm1fbDuLxT7N+7o5S6Rjd9d2yj14YKXOeUcHT0Gew+cpLPqo/z5mcHOXiiidxMGxOG5DGsfzZjBvZm8tC+jC7s3X2BrXCoqODgf9bx/ODJvFKrsvOQEcNXBOTnZNAvK52+2Wn0zUqnT680CntncPqgXM4b2b+lTHQwvPP5IX707EZEQz13rn2K63aspvK3T7HycC77B+Wwsf4UR+pd5PZKoyg3k8snDuKmc40U2tqTbq5dUtEiWyCEgN/810T+a/KQyNrDwiKOdFXrJqWKms36XTm7j5zscpt3fjiDUe2s+kMnmvjes5+wwXmM8YP7cO/lZzB5aF8a3R5e3ryf/33tc041a/zwwtEsmOYgK01tWZjEefQkFbtr2H7gBF8crKeyphFFQEaayoHjp2hqbq190qdXGq/cPo2h+dl8vv8Et/xtA4dOuLD368W1U+zcfK6DPllpaLpk2/7jbNhzjA921bBhzzFyM23MGlvIf022M3FIn25HGJou+WDXUV7dsp8dB+vZfeQk9S5j/dv87HRmjC7gqjMHM21k/+hktFRUUHnV9cy94becTO/F1HwbM6eM4KzivkwY0qelg4sWlTWN3LF0LRtqPOTYoFEDXUK6qjDF0ZfhBdkcP+Xh1S2GS2jHfXOwqQqvf3qA257dxI8vGs3YgbmcdHtocHk46fJgUxRGF/Vmgr1PSB2PhYUZ6DFFzX42dyyL/rGZBq9Cmz9+IEcaXLg9OjUnXTjysynM7egaeH7jPjY4j3H5xEE8cu2kFsWXlW7j+qnFzBpTyP97cSu/e2cnv3tnJ2BUJdSlxFfKPifDxuiiHM4fZQTVTjVrzB5TyLCCbIb3z+F7f9/E0QY3h064GJqfzemDcllz1wW8te0Qf3t/D799eyfbD9bz6A1noSqCCUPymDAkj29OHx5WW6iKYPqogpYsCykle2sa+chpdB6rth/ixU+qGZCbyfwJA5k9tpCzHf3azPIMifJy9vXqy8n0Xvz8vSf55pVnwwWXhHesICjOz+Ifd1zMezsOs2r7YfrnZHDO8H6cVdy3xVX1t/f38OqW/SwocbR0zFleF8fIwhwuTHA5AguLeJFSiv7C04vY+IsLufNfn/LKlv24PDqlpxVQ3C+LqcP6dVp3+7IJg3h2fSWvbNnPvtpGfnn5uDYr2RflZvJ42RTWfHmUHQdP0NSs09SsoSqCwtxMpo3IZ1j/7E6t7Dv/tYWjDW5+Nm9MmzK1GTaVMwblsqmyDoCzh/aNXmO0QwiBo382jv7ZXDPFTlOzxn++OMy/N+3jqYq9PLluDzkZNqY4+jJmQC65vWz0zrCRnWFjQJ/MNgo0IKWlrHvXcJeNrd0HpXfG7Fr8r2nWmCJmjemosE+5NX698gsA8nPS+f07O9m49xjrdx8jXVUYVhCaG87CIplJKddNU7PGg298wef7T3SoNpiXlcbauy7odKJJU7PG3zdU8ufyXbg9Out/NjtqQczv//0TXvt0P5dNHET/nAw8mk69y8O+Y6f4pKoWRQh+Nm8sC85zROV8odLg8vD+V0cp33GETXtr2XWkoaWeuo9+2ek8+LXxXHzGgIDH+PJQPfP/sIZL1Vp+P29Ewous6brk+sc/ZP2e1vtgzIDenD+yP2UlDqtMr0XK0WNcN2t2HuFvHzgDflfX2My6L48yojCHkQU5HYKRQsCEIX04fWAuq3ceYcOeY2FNdQ/EvZefgcujsamylpoGN+k2hex0G4PyMrn5XAffKR1BQe+MqJwrHHIybFxyxgAu8SpxKSUuj059k+G/3n2kgd+/s5OFT23k5/PHdnAnVded4kf/3EKvjDR+9uNrICdx1+JDUQTPLTyXRrcx8lKEsNYDtuixpJRFr+mSZe/v4c/luzh2svO637mZNs4a2pdpI/pz7VQ7rmad65ZUsMsbyB1dlMOz3zqX/iZQWGbB7dH57jMbWbPzKIsuGkVer3QOnWhiU2UtH+yqwaYI/nj9mS2dhYWFRXzpMVk3PtwenZ2H6umVrjK8fzZNzTrbD57Apgh2Hmpg495jfOys5cvDDWSmKUhpWPR3XjKGyyYMDBiwtTBmr3776Y1s228UiRPCmGQ0b/wArp9azJC+ljvEwiJR9DhFHyyfVR/nhU37cHl0bphazLjBfbrfyYK6RjeNbo3+ORmWO8TCwiT0GB99qIwb3MdS7mGQl5VOnmW8W1gkDZY5ZmFhYZHiWIrewsLCIsWxFL2FhYVFimMpegsLC4sUx1L0FhYWFimOpegtLCwsUhxL0VtYWFikOJait7CwsEhxTDkzVghxBNgbw1P0B47G8PixwpI7vlhyx49klBnMJfdQKWXASoymVPSxRgjxcWdThc2MJXd8seSOH8koMySP3JbrxsLCwiLFsRS9hYWFRYrTUxX9kkQLECaW3PHFkjt+JKPMkCRy90gfvYWFhUVPoqda9BYWFhY9BkvRW1hYWKQ4KavohRCThBAfCiE2CyE+FkJM9X6eJoRYLoTYKoTYLoS4u5P9+wkh3hFCfOn93zeBMt/o/cz3pwshJgXY/14hRLXfdvNiLXOU5I57W3clt/e7CUKICiHENu+90mF9SbO1dwhym6q9hRAOIcQpv3b8ayf7m6q9Q5A7Ie3dBillSv4BbwNzva/nAeXe1zcAz3lfZwFOwBFg/4eBn3pf/xR4KFEyt9tmPLC7k/3vBe4wS1uHIHfc27qbe8QGfApM9L7PB1Szt3cIcputvR3AZ0Hsb7b2DlbuhLS3/1/KWvSABHK9r/sA+/0+zxZC2IBegBs4EWD/K4Dl3tfLgStjJmkrncnsz/XA3+MgSyhEKnci2ho6l/ti4FMp5RYAKWWNlFKLk0zBEKncZmtvsxOp3Ilq71bi3bPEsRceC1QCVUA1xvRggDTgOeAIcBJY2Mn+de3e1yZK5nbb7ALGdbL/vRgjlE+BpUDfRLZ1CHLHva27uUcWAU8BbwGbgLuSob1DkNts7e3wPoufAKuB6UnS3sHKnZD2bnPOeJ8wyj/Au8BnAf6uAP4IXO3d7uvAu97X04BnMBR+IbADGB6vHyccmf32PQfY2sWxiwAVI/ZyP7A0kW0dgtwxaesI7pE7gD0YdUyygApgttnbOwS5zdbeGUC+9/VkDIWamwTtHazcMWvvoK8v3ieM24XBcVrnCQjghPf1o8DNftstBb4eYP8dwEDv64HAjkTJ7Pf9I8DPgjyWgyD8h2aQOxFt3c09ch3wN7/tfgHcafb2DlZus7V3gO3KgSlmb+9g5U5Ue/v/pbKPfj8w0/t6FvCl93UlMEsYZAPnAl8E2P8VYIH39QLg5RjK6qMzmRFCKMA1GG6ngAghBvq9vQrDIokHEclNYtoaOpf7LWCCECLLG8uZCXzefmcTtndQcmOy9hZCFAghVO/r4cAoYHf7nc3W3sHKTeLau5V49yzx+gPOBzYCW4D1wGTv5znAv4BtGA/BnX77PIG3R8bIWFiF8aOuAvolSmbvd6XAhwH28Zf5KWArhg/zFbxWRBLIHfe2DkLum7z3yGfAw0nU3sHIbar2Bq72yrwFI7ZwWTK0dwhyJ6S9/f+sEggWFhYWKU4qu24sLCwsLLAUvYWFhUXKYyl6CwsLixTHUvQWFhYWKY6l6C0sLCxSHEvRW1hYWKQ4lqK3sLCwSHH+f9cJmvr13TIuAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#ALL TRIMS COMPLETE. let's convert the partisan data to the format we use for HD code\n",
    "# in this prototype, we only use 2020 presidential data, but this could be modified\n",
    "vtdGOP = [0.]*nPrecincts  #Each precinct's R/(R+D)\n",
    "vtdPop = [0.]*nPrecincts   #THIS IS VOTER POPULATION\n",
    "vtdArea = [0.]*nPrecincts\n",
    "vtdCPx = [0.]*nPrecincts   #these are centroid x and y of each precinct\n",
    "vtdCPy = [0.]*nPrecincts\n",
    "plotcolor = ['blue']*nPrecincts\n",
    "for p in range(nPrecincts):\n",
    "    vtdPop[p] = vtdTrump[p] + vtdBiden[p] \n",
    "    vtdGOP[p] = vtdTrump[p]/max((vtdTrump[p] + vtdBiden[p]), 0.01)  #avoid divide by zero\n",
    "    if (vtdGOP[p] > 0.40) :\n",
    "        plotcolor[p]='purple'\n",
    "        if (vtdGOP[p] > 0.60) :\n",
    "            plotcolor[p] = 'red'\n",
    "    vtdArea[p] = vtdGeom[p].area\n",
    "    vtdCPx[p] = vtdGeom[p].centroid.x  #not needed except for graphing\n",
    "    vtdCPy[p] = vtdGeom[p].centroid.y   #not needed  \"  \"  \"\n",
    "    if isSkippedPrecinct[p] == 0 and p%10 == 0: #memory drain if we print all\n",
    "        plt.scatter(vtdCPx[p], vtdCPy[p], marker='.',c=plotcolor[p])\n",
    "for p in range (nPrecincts):\n",
    "    if p%100 == 0:\n",
    "        print(p,vtdGOP[p],vtdPop[p])\n",
    "x,y = wholeMAP.exterior.xy\n",
    "plt.plot(x,y)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "id": "a1e3f0d2-c21c-46d4-8227-e75dfd5e89b6",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "original and final map areas are 9.877842431734967 9.56265621703348\n"
     ]
    },
    {
     "data": {
      "image/png": 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ikicieUVFRfUMyxhjWq6KKj8EEkiO3OgHQD0SvYhcDBSqav4JHD/crcSw/7pUdYaq5qhqTlZW1gmcyhhjWpYDJU7/+Ugn+vr00j4DuERELgSSgDQReRnYLSJdVHWniHQBCsOU3Q50r7WeDRQ0NujWRK13pTFxa/+hCqANKSmR7V533Bq9qt6tqtmq2gu4EvhEVa8F3gKmhnabCrwZpvgioL+I9BaRhFD5t5ok8lbBsrwx8exQqdN/PiUpsj3dG3P03wHnich64LzQOiLSVUTeA1BVP3AL8AFOj53XVXVV40I2xpj4cKCkEoDUlMgm+gY9YK+quUBuaHkvMDHMPgXAhbXW3wPea0yQxhgTjw6WODX6NqnuiJ7Hnow1xpgoKS7zAZCabIneGGPiUnGp80Rs29TIjl5oid4YY6KkpMxJ9G1SLNEbY0xcKq8MAJCSZIm+VbN+9MbEL5/P+QNP8MZu90oTaWJZ3ph4Fgg4f+Nud5QfmDLGGBMZ1Z/YPS6r0RtjTFwKBK1Gb4wxcS3o3IvF5bJEb4wxcSkYartxW6I3xpj4FAzN8OGK8OTQluhjnXW8MSZuVbfRe617ZWtmWd6YeFZdo/e4LdEbY0xcOtyP3trojTEmPlmN3hhj4lxz9aM/7kg6IpIEzAUSQ/u/oar3icipwDNAG2AzcI2qHgpTfjNQDAQAv6rmNFn0xhjTgsVSr5tK4BxVPRUYBpwvImOA54DpqjoUmAnceYxjnK2qwyzJG2NMjcpKp0afmuSN6HnqMzm4qmpJaNUbeikwEKemDzALuDwiERpjTJyqCCX6tqkJET1PvdroRcQtIkuBQmCWqi4AVgKXhHaZAnSvo7gCH4pIvohMO8Y5polInojkFRUV1fsbiHc2TLEx8auiwvma0SYpouepV6JX1YCqDgOygVEiMgT4HnCziOQDbYGqOoqfoarDgQtC+59VxzlmqGqOquZkZWU19PuITzZMsTFxrbISkABJCTE08YiqHgBygfNVdY2qTlLVEcCrwMY6yhSEvhbitOWPakzAxhgTLyoqAE9FxM9z3EQvIlkikhFaTgbOBdaISMfQNhfwC5weOEeXTRWRttXLwCScJh9jjGn1qioF8dTVGNJ06lOj7wLMFpHlwCKcNvp3gKtEZB2wBigAXgAQka4i8l6obCfgUxFZBiwE3lXV/zb1N2GMMS1RcyX64zYMqepy4LQw258AngizvQC4MLS8CTi18WEaY0z88flciMcX8fPYk7Exau7SbVCZFu0wjDERFPALrmZI9JG91WvqJRhUHnlpMf96sxifT9i1LYWiZSOjHZYxJsL8PhcudyDi57FEH0XBoJK7ZCvf/d8dbPv8dPCUI55KVF2cdP5cBgwIctNVPaIdpjEmQgJ+Fy6vP+LnsUQfJQV7iunR/xCBAz2BngyaNJcF/zeatNSM0B5hHzcwxsSR5qrRWxt9lLRJTiCzR8Hh9fPO9ZCWmhjFiIwxzS3gd+PyWKKPW2mpiazKHXx4/czh7fnpY/OZv3JHFKMyxjSngN+N2xJ9fOuYmcp5N+YC8D/nDuQPt49l/DmRvwNvjIkNQb8LlycY8fNYoo+yD2dM4NGXl3Da5DkAJLQpOU4JY0y8CPrdeKxGH/+qfAECQeWrNU6f+RHj9kQ5ImNMcwn6Pbi9ka/RW6+bKAgGlaUbdjNmXDm+Q+2gYji4Kznz2lzefeb0aIdnjGkmwYAbtzvyo9Rajb6Z5a3ZidstjBjYGV9hb6hsw1XT57JtZyXzXppAm+TITkBgjIkdGnTjaoZEbzX6ZvT0v5Zx6xW1hv5x+dhZVEnndtZn3pjWSFUQl9Xo48qbH+4/vOzpsIW9+/10btcmihEZY6JKIcLzggOW6JvV1Ms7HV727+lJ+/RkTr5w7jFKGGPiTUWVn4deyGPAufPwH+yEqxlmkrOmm2Z0xcQBzPrFPPIXK1WVwvqPx/Hl+2fR6bSFnH9xJX/8+WhrozcmDlVU+fnnrHU89XwhS2adRLA4B5IO0uW0pdz14/SIn180BmefzsnJ0by8vGiHEXHfvDWXTz9Kp3RfBr7C3rgzdnD3b3bw6x/abIvGxIspd87hjSdywJcK7io6n7aEa68Jcu+Nw5t02BMRyVfVnLDvHS/Ri0gSMBdIxPkE8Iaq3icip+JMH9gG2Axco6qHwpQ/H2eCEjfwnKr+7ngBt5ZEXy0YVC798RzeeXoC7szt+PdlRzskY0wTSey0iarCPiR3X8PcD9PJGdQlIuc5VqKvTxt9JXCOqp4KDAPOF5ExwHPAdFUdijPp951hTuwG/ghcAAzGmX5w8NH7tXYul3Dtpc4PP7Dfkrwx8eSttwR35nbKtw1i5EldmHrvPPyByD8kVdtxE706qp/L94ZeCgzEqekDzAIuD1N8FLBBVTepahXwGnBpo6OOQz061/S++evbNn+6MfHiG6N7U7WnG795IZ+2/Zbz9wfH0eHk5c06gGG9et2IiFtElgKFOJODLwBWApeEdpkCdA9TtBuwrdb69tC2cOeYJiJ5IpJXVFRUz/Djx/L1+w4v33DJEPqd8ymX3TaHBasKjlHKGNMSuFzC3d8dwYG1Q7nuF/M4uHEQpw/P5NmZK5rn/PXZSVUDqjoMyAZGicgQ4HvAzSKSD7QFwk1lHq6HaNibAqo6Q1VzVDUnKyurXsHHk5u+NZSf/2kBiV3Wk9hlPV8tGsDMx8czZkQqI6fkUri/NNohGmMayeUSXnjgTKbevQh8KTzyxMHmOW9DdlbVA0AucL6qrlHVSao6AngV2BimyHaOrOlnA1ZFrcNDPxxNRUF/Kgr6EyjuyH/mbIDKdPLemMB/5myKdnjGmEYoKa/iu7+cR2rPdbz463G42hRy281tm+Xcx030IpIlIhmh5WTgXGCNiHQMbXMBv8DpgXO0RUB/EektIgnAlcBbTRR73CsoKju8fMMlQ6IYiTGmMbYXHSJrwFe8+OtxEBSuv28e+3elc8uUU49fuAnU54GpLsCLoR40LuB1VX1HRH4sIjeH9vk/4AUAEemK043yQlX1i8gtwAc43SufV9VVTf9txJdgUDnrujl89sqEw9tcrmZ4TtoYExGL1xZSsb0/AF36FzL/C+Wsq78gMUlJSYGRpyVx3cV9GdInMs3W9sBUDHpj9jqmnDPg8Pp3fzmPFx4YF8WIjDGN9cBfFvHY772U7mlHsCoR9SWi/kTwJzs7SIAJU+cx+4UJJ3T8Rj0wFQ2tPdGL2w9B58PWg8/ncc/1YX92xpg4sHJTERddt56tn51Oau+VlGw6sWbaxj4wZZrZRT/89PDyL76Xw659Nr2gMfHm0+XbGXLRXIYOSjmc5F98PjJjXVmij0HvPD0BVRhw7jwAbrq/9X66MSZeXXbdTla9dxadhq7ktVlrKdk0hMsnDDh+wRNgo1fGsOuuTuYXH8Fbz4xiUkUuf/9djo1fb0wLMn/lDl56ezMHiwN07eglu3MyPbqk0jc7nRtuDPLbWwPs2dSdIX29EY3D2uhj3Mw567lsgnO3vjHtd8aYplNW4eMv/1nFX185SNHOJAJ+Fwi0Ta/E5VYO7k1i76YeBIs7Hf9gwF1PfsHDt45pVEzHaqO3Gn2MW7b2wOHl0q8syRsTDduLDvHSu+v5ILeYFXlt2bd+AFQNA28pSZ2243IHQGHfli6ggje1hF4j1nPa8DVccWFn+nVPZ9OOQ2wpKGX7rnJ2FvooLFLKy6FbV+HeG8ZGNH6r0ce4YFA5+YJ5rPnwLEgoQSut6ca0DiXlVfz+5WW8/3Epu3Z48XiVDh39dOsqVPmUvXth/14Xhw4kUHowicriFHzlKQSrknB5K/EklZOZvZchw8r564On0aNT/Sf4KCmv4unXV/CvN4v5clEXKnb0A3WDBEjOXk/fU3ZzwaREbr92SMw0p1qNvgV77NWlrJkzDIAb7lkKnBnNcIxpFq9/vJbrvuOicudIcFfhydyJ+j1sLM6CQKhnisuPK2Uf3jaHSEwrJbPbPlLaFJKUHKSyQigt9rJ3WxYf5fWm9+u7uePB1fz25tHHfPjw37nr+NVjO1kx6xS0fAR4Ksgc8CWjJ87j/LPbcu2F/cnOGgQMap4L0USsRh/DthUepO/JBwlUJjL3Ux9nnGJj1Zv4t3brXgYN8ODyVvKTX23k3htPI6NNEgD+QJC1W/fSNiWB7Ky0ej0x/vf3v+SmaW4qtg8kpeeXjD6nkDEjkklKdLH/oI/de/wszoevVnSjaldfcFfSY0w+117j5rarh9IhPSXS33KTsAemYty+fTBnDmzcCNu2wc6dsG7LIZYt8YAvif+ZnsuPfuDh9VWvU+YrI9GdyOo9q5l66lSuP+36aIdvTJO6988LefBHoxh8wVyWvX0mHnfje4GXVfi44YH5zHy5U2gogiOPKUkH6DBoPWdOKOPh24fSv3u7Rp+zuVmij2EffwyXXgqloVGI27SBbt0gKWM/y/RlUka+TkafTRQUF5DqTaXUd+RwxYFfBnCJPQ5h4seBkgoGn7OUnYvGMPTiOSx/e3yTHn970SE+XrgdXyBIVkYSndunMGJQ5yb5hxJN1kYfgyoqYMIEWLAA2reH99+HU06B9MP3izK58o3PeHf9UsZmf4PJgybzrUHfIn9nPuP/VvOLHwgGcLXwX1Bjastok8T2L0aTedIy1izo0eTHz85KY+pFrWtGU0v0UbJihZPkAfLyoFevr+/z6uWvEtQgbpf78LacrjX/sDumdsTjsh+hiT8ul+ByBUlKL452KHHBqoJR8MUXMGpUzXqnOp6pEJEjkjxAijeFFye/SGZSJoWlhVz++uX4Ar4IRmtMdHTrU0zx1j68/4VNutNYluib2YIFMPaoZyM2b27YMa479Tp237GbjKQMZq6ZScKDCVT4K5osRmNiwUM/64p4K7jo7Hb89e2V0Q6nRbNE30yKi+HJJ2FM6Cnnn/8cPvgA1q+Hk05q+PG8bi+779h9eP2lZS81UaTGxIZLz+rH3M8rcCWX8L+3WqpqjOM28IpIEjAXSAzt/4aq3iciw3CmD0wC/MCPVHVhmPKbgWIgAPjruisc7375S3j8cWf5jTfg8stP/FgLti/g1ZWvkp5Y86Rfj/Smv2llTLQdKK7Em+KnYl/7aIfSotXnTl4lcI6qloiIF/hURN4HfgU8oKrvi8iFwCPAhDqOcbaq7mmSiFuoiy+uSfTvvQeTJ4PbfawSdXsm/xn+tvRvCM7DIqd3P50JvSY0RZjGRNX6bfuY/tgKFi3wUvBlTwIHnAeYvv/LRUD9BggzX3fcRK9OR/vqmS+8oZeGXmmh7elAQSQCbKmCQTh4EObPd7pOPvtszXuvvw5/+EPtrpQNPLYG6Znek43/u5GC4gKy07IRsTllTcv2xux1XHFxe7RsPO6MHXQ9aQsjx2zgV7cO5uTeNvRHY9Srb15oYvB8oB/wR1VdICI/AT4QkUdx2vpPr6O4Ah+KiALPquqMOs4xDZgG0KNHy2uGCARgxgyn1r59O5SV1bzn9cIVV8C4cVBeDt/85okneXAS/ZaDW1i4YyE5XXMsyZsWKxhUZi/eytMvb+Y/zw7D5ank9U/W8e2zBwDdoh1e3KhXolfVADBMRDKAmSIyBCcp36aq/xaRK4C/AueGKX6GqhaISEdgloisUdW5Yc4xA5gBzpOxJ/btRM/DD8M999Ss33svpKXB8OHODdiUJhwuIy3B+SB1+vOn88i5j3DnGXc23cGNaUL+QJCX3l/NO5/sYfNmpazMRXmZm8oyDyX7Uyjd1RUt6wn0JH3gUv79ciYTcyIzy1Jr1uAhEETkPqAUuBfIUFUVp0p5UFXTjlP2fqBEVR891n4tcQiE99+HH/wAtm511ufNgzMj9GmzKlBFfkE+414YR0ADnN79dFSVYZ2H8aeL/hSZkxrTQLc+/DnPPNId/77uzoaEElwJZbgSy/EkVZCcXkqHLqWMGaN8+/yuXHJmv+gG3MI1aggEEckCfKp6QESScWrtD+O0yY8HcoFzgPVhyqYCLlUtDi1PwrmJG3cuuAAeegi+8x2nNn90X/mmlOBOYGz3sdx5+p3k7XT+IX606SPmb59vid7EhMtum8PMx8eT0uNLvn/7p0yb0o/hAzoDsTF2e2tTn6abLsCLoXZ6F/C6qr4jIgeAJ0TEA1QQal8Xka7Ac6p6Ic5t8pmhNmQP8A9V/W/TfxvRVVYGv/kN/L//Bz16OH3kT7RHTUP89tzfHl7+2ayf8eTCJyN/UmOO454/L2Dm4+PoOGwh2xaOIMHbDH8M5pjq0+tmOXBamO2fAiPCbC8ALgwtbwJObXyYsWf3bmeMmtWr4emnYcsWuPZaJ9knJTV/PEEN2iiWJuqCQeXRBzqQ2HkTq+cMtSQfI2xErAbavBl+9St4+WXwhYaYGTECXnwRxjftaKoNYonexIK/vLmSqt1Duey2ObRLszb3WGGJvgF27oRzzoGCApg2Da66Cvr1g44dIdo9HANq49Kb6Fr1VRE3fz8Td8YOfnfb0GiHY2qxRH8MS5bACy9AdjbccQf89rdOjf6zzyJ7s/VEWI3eREswqNz5xBc8fn8vgmXteentzfTvbn3gY4klepwnWG+8ERYtcqbyu/FG+NGPnNr7gQPOPiNHOlP+qcJpX7tj0bzyC/J5b/17+II+fAEfvqCPT7765Iixb4w5EYdKK1mwaifL1+9n9cZSfD7logkduWxC/yNmYFr1VRF/f2cjH82uYNX8bCp3jSW5+xqeenkv154/JIrfgQmn1U8l+OGH8I1vfH27CGRkOH3h337b6U2zdavzVOtbbzVLaHWa9NIkZm2aBYDX5cXr9uJxebhm6DXWvdKcsCt/Nod/PpYDvtSw71/ww1w2bXDx1bJsqgr7OBs95WT0X8M3Lytmxi9PJynB6o7RYlMJHuXGG+G55+Cll+Duu51tIjBzpjPYmNfrNNXceiskJMB99znDGtx6K9x8c1RDB+BAxQEm9Z3Ef6/5rw1/YJrEqx+u4Z+PjCNj0DKmXFPKwN6pnDaoHaow/eGvyH9/CO//eQKSdICsQesYftlWJk9qz1WTBpCWGuWPuOa4WmWiDwScr9/5Ts22N95wJumurASXCzy1rszTTzdvfMdTXFVMz4yeluRNk9iy6yDTf7UPcPH6i5mcN+rIxL3oXz0B+KrgAN07peFxjwpzFBPLWuXdu+Rk5+vVV8NPf+q0w192mbMtIeHIJB+LSqpKaJvQNtphmBZu1sLNDLt0Dr16CVs/O53xU3M5b1SvOvfv3TXjiHZ603K0yp/a6NHO15Ur4ZRTamr4LUVxZbEl+hjyg4c+xdthC5JQSlKXDUz8fi4LVsXuqN2fLt9Op9MWMml0L5a9O5beY5bz2qy15P5tQrRDMxES43XXyLjuOqdW/8tfwtSpTvv8+PGQmQmlpfDgg04vm2jLL8gnf2c+HpcHj8uDS1wIQnFVMW0TLdHHimd/dSr4k+k+ZiGH9qbwyfMTGPN8kJQeXzJ0bCFXTc7g5imnxERt+IV3VvG9KdmgAzn7+lwev2cwp/S1sd7jXavudRMMOhODfPAB/PrXNdunTHEmB4m2Mc+NYcGOBWHfe+aiZ7gp56ZmjsiE0/G0RRQtDdUMPBUQdEPQ66y7fBD0kth5A48+WcotU6I7Ikj7ofkc2NqNj3OrmHBay5v3wdTNet2EsX+/M358YSGk1upN1r69M15NLBicNfiIRL/ulnUoilvc9MnsE8XIWpeKKj8P/XUxK9eW4/XCVRd35Vvj+wMwc856+g4q5eDOjVTt7Qb+moGOssfMZ8HbQ3jwLwuZ8f96cusVffD+3wpu+lb0nhot3ZNOl5O+YsJpMfbEn4moVpPoVWHOHOehqJISeOUV2LgRunaFvXth4kTnxuz550d/OANwxpyfu+XI+Vn6tetnPW2aWZUvQKehqzi0rqanyb8ehbb9ljN6wj4+emEsaG8k+QC4/M4OLj+Zg5bzyp+z6dqhLX+6+wyK9s3hjUez2VJQFv5EzSQpvYQDhdbs19q0mkT/2mtOL5uj7d4NS5fCkBh7mM/r8tIxtSMb928EwOPyWJKPgpLyKg5t6Q1ASo8v6XXyHtLSg+R/3IuPnjuFhI6bWPR5G07p2xFw/jF43G5cruFHHOedf3QhucdqHvxhdLsmdu93kJUfjCYYVFwu+31qLaJ/d6iZ9O1bs9y1q/OkKzg9buZ+bWLD6BMRPv/+5zxx/hMA+IN+Xl7+cpSjan3apSXz5keFjLoiF19FMl++fxZFuz2U7ezB7MVb2b+l++EkD5DgdYdNoJ6kStTvpsofvS5en+RvYeW740nqtMWSfCtTnxmmkoC5QGJo/zdU9T4RGQY8AyQBfuBHqrowTPnzgScAN86EJL9ruvCPb9Ys+OtfneaZUaOcZpktW2qm/Pve9+CmGL6nOa7HuMPLZ/U8K4qRtA5L1+9mwcpCdu+tYM9+H3v3+9ldGGTfHjf+Umd2pFGjg3jcrgbdzLz+B8U8dddQ0ntsZPSkbVz37Q5cd8FJzTpe+013bQV68o9XWk39zlRT1WO+AAHahJa9wAJgDPAhcEFo+4VAbpiybmAj0AdIAJYBg493zhEjRmhTGT5c1Wmhr3ldd53qY4+prljRZKeJmEAwoNyPcj/61IKnNBAMRDukuPXPj9Yo7sqv/b6AqqQWaY8zPtPfvJB3wse/7fefa9t+yxTxHz7m8G/N1l//dZFu3nmgCb+Tr9tfXK6e9ls069SFET2PiR4gT+vIqfWZYUqBktCqN/TS0Kt6MvB0nDlkjzYK2KDOTFOIyGvApcCX9f9X1DgPPwzTp0N+vrM+bRo8+2xznb3xXOLilpG38PSip7n1/VuZ2HsiJ2WdFO2wms2eg2WkpSQ2S833+X/thMBA7nziCwb0akPn9sl0zUqlT9cMMtp0ADo06vh/uH0sf7gdNu7Yz5Ovrubf/xYWv3kGi2d6uRdI7LyBDr2KKDuUSOmeDHylbVFfEuKtJCF9P136FTL5EuGn1w0hOyst7DmqfAHWb9/Hhm0H2bi9mG07y8lbWsX8d/sR2N+D79yzo1Hfg2mZ6tWPPjRfbD7QD/ijqv5MRE4CPsCp8buA01V1y1Hlvg2cr6o3hNa/A4xW1VuOdb6m7ke/YgWceqrzFOySJbHRq6ah/pL/F6a9M41VP1rF4KzB0Q4nYtZu3ctDM1bxxRfC1i87U7mzL3gqSe66mb5DC3nnuWH07ByZ4ZgnTctl1l8mUFxWRZvkhIic42gFe4p59YMNvD/7IMvzUzlY0J6EtqWkZ5WQnukjKSlIZaWLPbsSKVzbBy3NAmDUFbks+OcE1m/bx9lT1lK0uSO+4gy0LJOv33oL0u7kJdx5J0yf+rXZP02caHQ/elUNAMNEJANnsu8hOJOB36aq/xaRK4C/Aucefe5wh6sjyGmhY9KjR9M+yNGrF1x5Jbz6qtPFcsKEJj18o6xd6zydu3mz88/oqadgwICv75fiTQGc3jfxaO7Sbdxy7yZWfDASfGchKfvoMGAjgyfNpaQENq9rw8r3zyDnG0vY+PkppKUmNnkM1RWAKl8Akpv88GF17dCWn15zGj+95vj7VvkC/PZvi7h/2kgWvj6BcQm5lBQLOxaMp+OwhWSN2EH7DkE6dhS6dvLSo0syvbqlMqx/Fn27WYJvzRqUNVT1gIjkAucDU4Efh976F/BcmCLbge611rMJ38SDqs4AZoBTo29IXMeyapUzUYjP5/whe71NdeTG27ULfvITWLDA6fr5j3/AwIFOq/DRAur01ig95OX3f4OC3RUsXbufviM3UloWJCXJRbfOiYwa2p5zc3q2iEmZ9x0q57VZ63n4sRK2fj4aXJ3oP2EBD97dkW+fPQCX68hxKKbcOYc3Hh1PehsoOlBGh/SUJo3n5IGJfAiMu3IRs18ZQcfM8OOyR0uC1819N47k+5MPceblK/j05QnOG95SVuUOafLrYeJIXY331S8gC8gILScD84CLgdXAhND2iUB+mLIeYBPQm5qbsScf75yNvRm7b5/qxRerpqWpjhzp3Ezr2VN15crjlz10SPXpp1V/8hPVBx9UnT1bNRhsVDhhvf12zY2+++5TraioiXXSJNWXX1bdvr1m/xeWvKBMT9POQ78Me7PwiFfCIW13cp7e9vvPdX9xedMHX0/rtu7V+2cs1PFTZ+ugSXO0y8j5mjZgqSZ03qCutJ2Ky+fEm3hQR06ZrYtWFxz3mGOvmq2gmtJzlc5btu2E4iqv9OmzM5fr395Zpe9+vlEXrS7QLbsO6LINu9XbcZOC6ojLZ5/QsZvTvz5Zq9f9Yq6+OW99tEMxMYBj3IytT6I/BVgCLAdWAr8MbT8Tp91+GU5PnBGh7V2B92qVvxBYh9P75p7jnU8bkejnzFH93e9Uf/GL8AnwiSfqLhsMqr7yimrXrs6+KSk15caOVV23ruHxBIOqGzeqzpyp+v77qsXFNe9985vOsSdPVg2EOtL4/aqPPqraqVPNufv1U502TXXixfsUT9nh7Te88LA+9e+F+tGizZq/dqfOXrxF//LmCr3hgXl6yiW56kovcHp2JO3Xdifn6ZCLcvXqu+foG7PXqs8fuZ47i1YX6Lk3zNbELutqrr341dV2lyZ2XacZgxZrt9Gf68Dz5ui478zWO5+Yr1t2NazHyc//9IWSeEAlab9eedccLS2v0i27Duh9zy7UkVOcc0vKHvV02KzpA5foVdPnHC67buveI2ML+wroDQ/Ma+pLY0xEHSvRx9WgZoMGOW3ex7JqFQw+6l5mQYHThj9vHowY4bSTjx3rjGT5j3/A7bdDWRk88IAzy1Rqas2Y9Rs2wOzZsGyZc9N340Zn4pKkJGdWqvLymvNkZjpDMPTt6+x/4YXOuXv3hu9/35mnNjPTeYhryRInno8/dh7oysiAiy5Svn11CWef0ea4D7xU+QL84R9Lef6lMgo2tqN0ZzZUOjcxJbWIXjlrufG7yfz0mmFN1syTu2QrZ4/JhKq2pA9cyujxB7jg7AyunNSPzu3aNMk5ap9r8lX7OLh2GCQegqpUUDe4K8kcuIquvUooPuhh62eng8tPeTkkJXi4/Q/zeeynY5n4/VzGjkhh30EfBw4FOHgoSIJXGDO8LVdO6kuPTjb/rmlZjnUz9ri162i8TrRG/9Zbqm3b1tTMpkxRPess1dGjVXv3Vj3vPNX9+79e7qGHnP2vusqpVR9tx46aGnj1y+NRdblq1tu2dWr+3/2u87riCtXbb1d95hmn+adjR2e/uXNrjltRofrqq045cMpESiAQ1I8WbdbvPzBPe4+fp5K0X0HVlV6gw781W3//ymItLa9q1Dl+/8piBdVL/3d2k8R8PIFAUO97dqEOvmCOjp/qfA97D5Ydfv/y23MVVE+bnHt42/0zFiqo3vHY580SozHNhdZSowdnKsDrr3d62FxyiVMz79MHzjoLOncOX2bTJjj7bOdp2bFjnRr9iKM6Kag6E4mvWOHU0svLwe12hlOYOBH696+72+b3vgcvvOCMinnHHUe+t3at80kE4MknnU8MzeFASQW//ssSXnnZxe5lwyCQCImHyBq0lj6DSsnIgIx0ISPNzYDeqXz34kG0Szt2V5TTr85l/qsTeOTvi7nzO8OPuW+k7TlYRlZ7NwQSOe/GXCorYUVeGvvXDAUJ8sZHW7h8QpjuTca0UK2mRl9Wpnrrrarjxn293bVdO9WDB49d9oknVDt3Vs3MdNabylVXObX/q69Wve021VtucZ7OHTdO1etVTUpSfeqppjtfQ+0oOqTTn/pCB31jjiZ2Xae4qsI+GXr301/UeYw3561X3BXaZ3xstG37/AFNH7jkiO8hqdsaHXHZbJ29eEu0wzOmydFaavRvvgmTJ9f9/htvOLXnk05y2tFrq6x0np799a/hv/91JiOZNKnBIYS1Z4/zRO7Spc7494mJ0KaNM7BaTo7z5G6nTk1zrqYQDCoHSioo2FPCzj2lzFtSyKMPtaV820lccmsubz454Yj956/cwbkXHaS8qAsrVvk5uXdWdAI/SjCoFB4oJcHjJsHrbraHoIyJhmPV6OMq0QcC8MQT8NvfOsm1LhkZThPNxIlwww1QUeE8RLVmjfP+ySfDJ59Ax451H6O1KSmvot+ZS9i9bBjfuGE+Hdq72L4jyMrFbdi7ahi4fEx/bDm/vWV0tEM1plVqNYm+WlWV07smJcV5yrS8HJYvd3rKrFoFn33mvL780pk7VtVpX//Nb+B//ge6dGnCbyaOfLp8O5MuLqZ8W/VYO0G8WVsYed4WHvlZP844JTuq8RnTmrW6RF9fixfD3//u1Ohvusl5gtYc38Yd+9lzoJyT+3Sw5hBjYoTNGVuH4cOdl2mYvt0y6dstM9phGGPqyWYgMMaYOGeJ3hhj4pwlemOMiXOW6I0xJs5ZojfGmDhnid4YY+KcJXpjjIlzluiNMSbOxeSTsSJSBGyJ4Ck6AMcYDSdmWdzNy+JuPi0xZoituHuqatgRBWMy0UeaiOTV9ahwLLO4m5fF3XxaYszQcuK2phtjjIlzluiNMSbOtdZEPyPaAZwgi7t5WdzNpyXGDC0k7lbZRm+MMa1Ja63RG2NMq2GJ3hhj4lzcJnoRGSYiX4jIUhHJE5FRoe1eEXlRRFaIyGoRubuO8u1EZJaIrA99jfhMG8eI+ZrQtupXUESGhSl/v4jsqLXfhZGOuYnibvZrfay4Q++dIiLzRWRV6HclKUz5mLreDYg7pq63iPQSkfJa1/GZOsrH1PVuQNxRud5HUNW4fAEfAheEli8EckPLVwOvhZZTgM1ArzDlHwGmh5anAw9HK+aj9hkKbKqj/P3AHbFyrRsQd7Nf6+P8jniA5cCpofX2gDvWr3cD4o61690LWFmP8rF2vesbd1Sud+1X3NboAQXSQsvpQEGt7aki4gGSgSrgUJjylwIvhpZfBCZHLNIadcVc21XAq80QS0M0Nu5oXGuoO+5JwHJVXQagqntVNdBMMdVHY+OOtesd6xobd7Sud43m/s/SjP+FTwK2AtuAHTiPBwN4gdeAIqAUmFZH+QNHre+PVsxH7bMRGFJH+ftxPqEsB54HMqN5rRsQd7Nf6+P8jvwEeAn4AFgM3NUSrncD4o61690r9Le4BJgDjGsh17u+cUfleh9xzuY+YRP/AD4CVoZ5XQo8CVwe2u8K4KPQ8hnAKzgJvyOwFujTXD+cE4m5VtnRwIpjHLsT4Ma59/IQ8Hw0r3UD4o7ItW7E78gdwFc445ikAPOBibF+vRsQd6xd70SgfWh5BE5CTWsB17u+cUfsetf7+2vuEzbbNwYHqXlOQIBDoeU/At+ptd/zwBVhyq8FuoSWuwBroxVzrfcfA35ez2P1oh7th7EQdzSu9XF+R64E/lZrv3uBO2P9etc37li73mH2ywVyYv161zfuaF3v2q94bqMvAMaHls8B1oeWtwLniCMVGAOsCVP+LWBqaHkq8GYEY61WV8yIiAuYgtPsFJaIdKm1+i2cGklzaFTcROdaQ91xfwCcIiIpoXs544Evjy4cg9e7XnETY9dbRLJExB1a7gP0BzYdXTjWrnd94yZ617tGc/9naa4XcCaQDywDFgAjQtvbAP8CVuH8EdxZq8xzhP4j4/RY+Bjnh/ox0C5aMYfemwB8EaZM7ZhfAlbgtGG+RagW0QLibvZrXY+4rw39jqwEHmlB17s+ccfU9QYuD8W8DOfewjdbwvVuQNxRud61XzYEgjHGxLl4broxxhiDJXpjjIl7luiNMSbOWaI3xpg4Z4neGGPinCV6Y4yJc5bojTEmzv1/Hcg5V2+lOrUAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "MAP = tractMAP  #committing to the trimmed map \n",
    "#bufferDistance = 0.02  #do minor buffer for OH.  default = no buffer\n",
    "origMAP = wholeMAP    # let's keep a copy of the original map prior to buffering\n",
    "#MAPbuffer = MAP.exterior.buffer(bufferDistance, single_sided=True)  #gives a little exterior buffer\n",
    "# MAP = origMAP.convex_hull  #alternate to buffer for weird state shapes\n",
    "bBox = MAP.bounds\n",
    "MAPMinX = bBox[0]  #these four are useful for slicing ops, and are basis for map grid (without buffer)\n",
    "MAPMinY = bBox[1]\n",
    "MAPMaxX = bBox[2]\n",
    "MAPMaxY = bBox[3]\n",
    "\n",
    "#MAP = MAP.union(MAPbuffer)  #not buffering for TN\n",
    "print(\"original and final map areas are\",origMAP.area,MAP.area)  #same if we didn't buffer\n",
    "x2, y2 = MAP.exterior.xy\n",
    "plt.plot(x2,y2,c=\"green\")\n",
    "x2, y2 = origMAP.exterior.xy\n",
    "plt.plot(x2,y2,c=\"blue\")\n",
    "plt.show()\n",
    "\n",
    "minTractPop = 10  #was zero to not exclude empty interiors"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "id": "8e271f55-e118-4575-bc99-c28c59f435b7",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2020 population, Trump+Biden voters, lean =  6785514 2972323 0.5819819637963837\n",
      "compare to Census 6785528 Leip has Trump + Biden = 2972368.0 0.5819814370226029\n"
     ]
    }
   ],
   "source": [
    "#let's confirm some population stats - these match 2020 census, USelectionatlas.org :-)\n",
    "censusPop = 6785528\n",
    "atlasTrump = 1729863.\n",
    "atlasBiden = 1242505.\n",
    "\n",
    "print(\"2020 population, Trump+Biden voters, lean = \",np.sum(tractPop),np.sum(vtdPop),stateGOP )\n",
    "print(\"compare to Census\",censusPop,\"Leip has Trump + Biden =\",atlasTrump+atlasBiden,\n",
    "      atlasTrump/(atlasTrump+atlasBiden) )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "id": "6c47f717-f208-4a4c-b825-a3181a83c6ee",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "state pop before, after slice operation are 6785514 6785514.0\n",
      "state Trumpers before, after slice opn are 1729834 1729834.0\n",
      "state Bideners before, after slice opn are 1242489 1242489.0\n"
     ]
    }
   ],
   "source": [
    "#Check on integrity after slicing\n",
    "sumPop = 0.\n",
    "sumTrump = 0.\n",
    "sumBiden = 0.\n",
    "for t in range(nTracts):\n",
    "    if isSkippedTract[t] == 0:\n",
    "        sumPop += tractPop[t]\n",
    "for p in range(nPrecincts):\n",
    "    if isSkippedPrecinct[p] == 0:\n",
    "        sumTrump += vtdTrump[p]\n",
    "        sumBiden += vtdBiden[p]\n",
    "print(\"state pop before, after slice operation are\",np.sum(tractPop),sumPop)\n",
    "print(\"state Trumpers before, after slice opn are\",np.sum(vtdTrump),sumTrump)\n",
    "print(\"state Bideners before, after slice opn are\",np.sum(vtdBiden),sumBiden)\n",
    "for t in range(nTracts):\n",
    "    if isSkippedTract[t] ==1 and tractPop[t] > 0:  #should not occur\n",
    "        print(\"missed pop for tract, pop, (x,y)\",t,tractPop[t],tractGeom[t].centroid.x,\n",
    "              tractGeom[t].centroid.y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "id": "c3eb2c10-5caa-42ce-a70f-d87d6e275e22",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2034 1\n",
      "0 0.00016990724566590647\n",
      "1 2.9354445031118904e-11\n",
      "2 4.989851763673341e-17\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#WTF is going on with this precinct?  #GA precinct triage\n",
    "print(p, notPolyVTD[p])\n",
    "counter = 0\n",
    "for geom in vtdGeom[p].geoms:\n",
    "    x,y = geom.exterior.xy\n",
    "    plt.plot(x,y)\n",
    "    print(counter,geom.area)\n",
    "    counter+=1\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "id": "2532d156-de55-4a64-ac38-1a373a9ab910",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#OK, kill the bad sub-polygons\n",
    "counter = 0\n",
    "for geom in vtdGeom[p].geoms:\n",
    "    if counter == 0:\n",
    "        goodGeom = geom\n",
    "    counter+=1\n",
    "vtdGeom[p] = goodGeom\n",
    "notPolyVTD[p] = 0\n",
    "x,y = vtdGeom[p].exterior.xy\n",
    "plt.plot(x,y)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "id": "2f6c72cd-90d8-489d-9b15-d4278ba17b3d",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "map has a total of 108 grids for 9 districts\n",
      "starting grid no 0 at x = -87.49906666666666, y = 37.94807516666666 \n",
      "starting grid no 5 at x = -87.49906666666666, y = 39.60573683333333 \n",
      "starting grid no 10 at x = -87.49906666666666, y = 41.263398499999994 \n",
      "starting grid no 15 at x = -87.17971, y = 38.94267216666666 \n",
      "starting grid no 20 at x = -87.17971, y = 40.60033383333333 \n",
      "starting grid no 25 at x = -86.86035333333334, y = 38.2796075 \n",
      "starting grid no 30 at x = -86.86035333333334, y = 39.93726916666667 \n",
      "starting grid no 35 at x = -86.86035333333334, y = 41.594930833333336 \n",
      "starting grid no 40 at x = -86.54099666666666, y = 39.274204499999996 \n",
      "starting grid no 45 at x = -86.54099666666666, y = 40.931866166666666 \n",
      "starting grid no 50 at x = -86.22163999999998, y = 38.61113983333333 \n",
      "starting grid no 55 at x = -86.22164, y = 40.2688015 \n",
      "starting grid no 60 at x = -85.90228333333333, y = 37.94807516666666 \n",
      "starting grid no 65 at x = -85.90228333333333, y = 39.60573683333333 \n",
      "starting grid no 70 at x = -85.90228333333333, y = 41.263398499999994 \n",
      "starting grid no 75 at x = -85.58292666666665, y = 38.94267216666667 \n",
      "starting grid no 80 at x = -85.58292666666665, y = 40.60033383333333 \n",
      "starting grid no 85 at x = -85.26356999999999, y = 38.2796075 \n",
      "starting grid no 90 at x = -85.26356999999999, y = 39.93726916666666 \n",
      "starting grid no 95 at x = -85.26356999999999, y = 41.59493083333333 \n",
      "starting grid no 100 at x = -84.94421333333332, y = 39.274204499999996 \n",
      "starting grid no 105 at x = -84.94421333333332, y = 40.931866166666666 \n",
      "done building grids. avgGridDensity, maxGridDensity,nPopulatedGrids = 622219.5455792152 6367399.753996266 103.0\n"
     ]
    }
   ],
   "source": [
    "# this section - ESTIMATE POP'N DENSITY at scale of 1/3 of district length\n",
    "# also, IDENTIFY WHICH TRACTS and VTD's INTERSECT EACH GRID to speed later intersection searches by grid \n",
    "# (above sections already pulled in Tract and Precinct geometry and demographic data, built an overall map)\n",
    "nDistricts = 9   #IN congressional\n",
    "avgDistrictPop = np.sum(tractPop) / float(nDistricts)\n",
    "#MAPMaxX = -80.   #In most state maps, we figure this out from the bounding box of the convex hull; see above.\n",
    "#MAPMaxY = 31.1   # ... but for Florida, we will do manually\n",
    "#MAPMinX = -87.0\n",
    "#MAPMinY = 25.5\n",
    "stateWidth = float(MAPMaxX - MAPMinX)\n",
    "stateHeight = float(MAPMaxY - MAPMinY)\n",
    "stateWHRatio = stateWidth / stateHeight\n",
    "G = 3  #adjustable parameter; G*G = number of grids that fit in an average district\n",
    "nGridsX = int( G*round((nDistricts*stateWHRatio)**0.5,0) )   #OK to have empty grids for a non-rectglr state\n",
    "nGridsY = int(round(nGridsX / stateWHRatio,0) )  #so grids are square even if state has high aspect ratio\n",
    "nGrids = int(nGridsX*nGridsY)\n",
    "print(\"map has a total of {0} grids for {1} districts\".format(nGrids,nDistricts) )\n",
    "nGridPrecincts = [0]*nGrids # will store how many precincts intersect with each grid square\n",
    "nGridTracts = [0]*nGrids    # same, but for tracts\n",
    "gridPrecinctNo = [[0]*nPrecincts for gridNo in range(nGrids) ] # initialize a list of VTDs that intersect with each grid square\n",
    "gridTractNo = [[0]*nTracts for gridNo in range(nGrids) ] # initialize a list of tracts that intersect with each grid square\n",
    "\n",
    "gridPop = [0.]*nGrids #will store approx total population for this grid square\n",
    "gridDensity = [0.]*nGrids\n",
    "gridGeom = [Polygon([(0,0),(0,1),(1,0)])]*nGrids  #initialize grid geometry\n",
    "gridWidth = stateWidth /nGridsX        #geo length of a grid's side length\n",
    "gridHeight = stateHeight /nGridsY    \n",
    "\n",
    "for nG in range (nGrids) : #  create polygon shape for each grid square, compute grid-square population density\n",
    "    x = int(nG/nGridsY)\n",
    "    y = int(nG % nGridsY)\n",
    "    point1 = Point(x*gridWidth+MAPMinX,y*gridHeight+MAPMinY)\n",
    "    point2 = Point((x+1)*gridWidth+MAPMinX, y*gridHeight+MAPMinY)\n",
    "    point3 = Point((x+1)*gridWidth+MAPMinX, (y+1)*gridHeight+MAPMinY)\n",
    "    point4 = Point(x*gridWidth+MAPMinX, (y+1)*gridHeight+MAPMinY)\n",
    "    gridGeom[nG] = Polygon([point1, point2, point3, point4])\n",
    "    if (nG %5 == 0): #print occasionally, should take about one second per grid\n",
    "        print(\"starting grid no {0} at x = {1}, y = {2} \".format(nG,gridGeom[nG].centroid.x,gridGeom[nG].centroid.y) )\n",
    "    counter = 0\n",
    "    #    Now for each grid square, find intersxn with all polygons, total the intersection population\n",
    "    #    Also, create 2 lists: of TRACTS and precincts that intersect each grid (efficiency shortcut)\n",
    "    \n",
    "    if( gridGeom[nG].intersection(MAP).area > 0.0001) : #don't bother w grids off the map\n",
    "        for t in range(nTracts):\n",
    "            # if (t%3000 == 0) :\n",
    "            #    print (\"grid {0}, tract no {1}, ({2},{3})\".format(nG,t,tractGeom[t].centroid.x,tractGeom[t].centroid.y) )\n",
    "            intersxnArea = 0.\n",
    "            if (notPoly[t] == 0) :  # tract is a simple polygon\n",
    "                intersxnArea = gridGeom[nG].intersection(tractGeom[t]).area  #replaced tractGeom w cleanedPoly\n",
    "            else : #tract is a multiPolygon, do the polygon intersections individually\n",
    "                #print(\"I think tract\",t,\"is a multiPolygon\")\n",
    "                for geom in tractGeom[t].geoms :\n",
    "                    intersxnArea += gridGeom[nG].intersection(geom).area\n",
    "            if (intersxnArea > 0 and tractPop[t] > minTractPop) : # this tract is at least partially in this grid and populated \n",
    "                if (tractArea[t] == 0):  #should not happen, but debugging\n",
    "                    print(t,tractGeom[t].centroid.x, tractGeom[t].centroid.y)  #debug print\n",
    "                gridPop[nG] += tractPop[t]*intersxnArea/tractArea[t]\n",
    "                if tractPop[t] > (1.0 * avgDistrictPop) :  #flag if we have a mega tract\n",
    "                    uhoh = input(\"Uh oh! We have a tract that's bigger than a district.  What now?\")\n",
    "                else :\n",
    "                    gridTractNo[nG][counter] = t  #add this tract to this grid's list, update the no of tracts in grid\n",
    "                    counter +=1            \n",
    "                    nGridTracts[nG] = counter\n",
    "        # OK, now, loop over PRECINCTS to develop each grid's list of precincts (prev loop was for tracts)\n",
    "        counter = 0\n",
    "        for p in range(nPrecincts):\n",
    "            intersxnArea = 0.\n",
    "            if (notPolyVTD[p] == 0) :  # precinct is a simple polygon\n",
    "                intersxnArea = gridGeom[nG].intersection(vtdGeom[p]).area  #replaced tractGeom w cleanedPoly\n",
    "            else : #precinct is a multiPolygon, do the polygon intersections individually\n",
    "                for geom in vtdGeom[p].geoms :\n",
    "                    intersxnArea += gridGeom[nG].intersection(geom).area\n",
    "            intersxnArea = gridGeom[nG].intersection(vtdGeom[p]).area\n",
    "            if (intersxnArea > 0 and vtdPop[p] >0) : # this precinct is at least partially in this grid and recorded votes                 \n",
    "                gridPrecinctNo[nG][counter] = p  #add this precinct to this grid's list, update the no of tracts in grid\n",
    "                counter +=1            \n",
    "                nGridPrecincts[nG] = counter\n",
    "    gridDensity[nG] = gridPop[nG] / gridGeom[nG].area  #finished loop over tracts & vtd's.  Calceach grid's population density    \n",
    "\n",
    "minGridPop = np.min(gridPop)\n",
    "# avgGridPop = np.mean(gridPop)   #see below for explicit averaging, since there are so many empty grids\n",
    "maxGridPop = np.max(gridPop)\n",
    "nPopulatedGrids = 0.\n",
    "totPopulatedGridArea = 0.\n",
    "for nG in range(nGrids) :\n",
    "    if gridPop[nG] > 0. :\n",
    "        nPopulatedGrids +=1\n",
    "        totPopulatedGridArea += gridGeom[nG].area\n",
    "avgGridPop = np.sum(tractPop) / nPopulatedGrids\n",
    "avgGridDensity = np.sum(tractPop) / totPopulatedGridArea\n",
    "#avgGridDensity = np.average(gridDensity) #avgGridPop / (gridPL * gridPL) #these densities help home district algorithm\n",
    "maxGridDensity = np.max(gridDensity)\n",
    "print(\"done building grids. avgGridDensity, maxGridDensity,nPopulatedGrids =\",avgGridDensity,maxGridDensity,nPopulatedGrids)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "id": "3f5a471b-867e-4db1-abdc-55f4b3d1f27e",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2034 0.00016990724566590647\n"
     ]
    }
   ],
   "source": [
    "print(p,vtdGeom[p].area)  #this will spit out the precinct number where we choked (again)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "id": "b538ed92-bf09-41ce-9758-8a764cbff950",
   "metadata": {},
   "outputs": [],
   "source": [
    "#sigh, this precinct's geom is still whacked.  Convex-hull it\n",
    "# vtdGeom[2034] = vtdGeom[2034].convex_hull  #after this line, go back up and redo the grid code"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "id": "808f513f-21af-4b03-8061-fdeeed33dafd",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "here is a heat map of grid density \n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<function matplotlib.pyplot.show(close=None, block=None)>"
      ]
     },
     "execution_count": 45,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "print(\"here is a heat map of grid density \")\n",
    "xyGridDensity = [[0.]*nGridsX for x in range (nGridsY) ]   #flipping x and y to get right orientation in pixel grid\n",
    "for nG in range (nGrids) : #  create polygon shape for each grid square, compute grid-square population density\n",
    "    x = int(nG % nGridsY)\n",
    "    y = int(nG / nGridsY)\n",
    "    if gridDensity[nG] > 0 :  #avoid log zero\n",
    "        xyGridDensity[x][y]=np.log(gridDensity[nG])\n",
    "c=plt.pcolormesh(xyGridDensity)\n",
    "plt.colorbar(c)\n",
    "plt.show"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "id": "4013f80b-4fc8-40a1-b708-ee29c29b3ffe",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "6.646599999964502e-08 4.0698264499997604e-05\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "10"
      ]
     },
     "execution_count": 46,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "print(np.min(vtdArea), np.min(tractArea))\n",
    "minTractPop"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "id": "ab450500-f1a4-4292-a2fe-71eb2217121d",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "I am working on tract number 0 of 1696 tracts\n",
      "we have 2 non-opposing shorted wedges for tract no 0\n",
      "we have 2 non-opposing shorted wedges for tract no 1\n",
      "we have 2 non-opposing shorted wedges for tract no 8\n",
      "I am working on tract number 20 of 1696 tracts\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 39 3.0 3 47.2 0.9233 213253.8\n",
      "I am working on tract number 40 of 1696 tracts\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 40 4.0 3 49.4 1.0236 195504.5\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 40 5.0 3 49.4 0.9958 195504.5\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 40 6.0 3 49.4 0.9687 195504.5\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 40 7.0 3 49.4 0.9424 195504.5\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 40 8.0 3 49.4 0.9168 195504.5\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 40 9.0 3 49.4 0.8919 195504.5\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 40 10.0 3 49.4 0.8677 195504.5\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 40 11.0 3 49.4 0.8441 195504.5\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 40 12.0 3 49.4 0.8212 195504.5\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 40 13.0 3 49.4 0.7988 195504.5\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 40 14.0 3 49.4 0.7771 195504.5\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 40 15.0 3 49.4 0.756 195504.5\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 40 16.0 3 49.4 0.7355 195504.5\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 40 17.0 3 49.4 0.7155 195504.5\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 40 18.0 3 49.4 0.6961 195504.5\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 40 19.0 3 49.4 0.6772 195504.5\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 40 20.0 3 49.4 0.6588 195504.5\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 40 21.0 3 49.4 0.6409 195504.5\n",
      "7 yoyos for tract,wedge,wedgePop,r= 42 3 246196.06190129177 0.9547\n",
      "7 yoyos for tract,wedge,wedgePop,r= 45 1 645499.5845385167 2.3186\n",
      "8 yoyos for tract,wedge,wedgePop,r= 45 1 91485.55614913593 1.1593\n",
      "9 yoyos for tract,wedge,wedgePop,r= 45 1 643228.2412447238 2.3065\n",
      "10 yoyos for tract,wedge,wedgePop,r= 45 1 173791.35073914303 1.7329\n",
      "11 yoyos for tract,wedge,wedgePop,r= 45 1 533692.8965805293 2.0197\n",
      "12 yoyos for tract,wedge,wedgePop,r= 45 1 312318.1914729745 1.8763\n",
      "13 yoyos for tract,wedge,wedgePop,r= 45 1 447341.9139157459 1.948\n",
      "13 yoyos for tract,wedge,wedgePop,r= 45 1 386345.41871013143 1.9122\n",
      "14 yoyos for tract,wedge,wedgePop,r= 45 1 348776.45064540394 1.8942\n",
      "15 yoyos for tract,wedge,wedgePop,r= 45 1 367402.1300808655 1.9032\n",
      "I am working on tract number 60 of 1696 tracts\n",
      "7 yoyos for tract,wedge,wedgePop,r= 66 1 329207.38655984256 1.648\n",
      "8 yoyos for tract,wedge,wedgePop,r= 66 1 76392.90763033504 0.824\n",
      "9 yoyos for tract,wedge,wedgePop,r= 66 1 354914.5948186014 1.7511\n",
      "7 yoyos for tract,wedge,wedgePop,r= 72 1 336504.9476228387 1.7017\n",
      "8 yoyos for tract,wedge,wedgePop,r= 72 1 67277.22714816395 0.8508\n",
      "9 yoyos for tract,wedge,wedgePop,r= 72 1 336710.76374622266 1.7032\n",
      "10 yoyos for tract,wedge,wedgePop,r= 72 1 138677.37465101713 1.277\n",
      "11 yoyos for tract,wedge,wedgePop,r= 72 1 299394.06545038486 1.4901\n",
      "11 yoyos for tract,wedge,wedgePop,r= 72 1 260954.90941700098 1.3836\n",
      "11 yoyos for tract,wedge,wedgePop,r= 72 1 213355.82936164853 1.3303\n",
      "12 yoyos for tract,wedge,wedgePop,r= 72 1 167765.21234824334 1.3036\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 79 6.0 3 56.1 1.3367 192087.5\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 79 7.0 3 56.1 1.3178 192087.5\n",
      "I am working on tract number 80 of 1696 tracts\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 80 5.0 3 50.6 1.2972 209106.6\n",
      "7 yoyos for tract,wedge,wedgePop,r= 81 3 204473.78564716078 1.0154\n",
      "7 yoyos for tract,wedge,wedgePop,r= 81 0 243122.25074225804 1.2845\n",
      "8 yoyos for tract,wedge,wedgePop,r= 81 3 34295.09412191596 0.5077\n",
      "8 yoyos for tract,wedge,wedgePop,r= 81 0 32253.470244556374 0.6423\n",
      "9 yoyos for tract,wedge,wedgePop,r= 81 3 204473.78078915513 1.0154\n",
      "9 yoyos for tract,wedge,wedgePop,r= 81 0 243122.0474703023 1.2845\n",
      "10 yoyos for tract,wedge,wedgePop,r= 81 3 98465.05946862404 0.7616\n",
      "10 yoyos for tract,wedge,wedgePop,r= 81 0 89819.4466824682 0.9634\n",
      "10 yoyos for tract,wedge,wedgePop,r= 81 3 133132.2211644083 0.8885\n",
      "10 yoyos for tract,wedge,wedgePop,r= 81 0 162968.18928736728 1.1239\n",
      "11 yoyos for tract,wedge,wedgePop,r= 81 3 203047.62509027068 0.952\n",
      "11 yoyos for tract,wedge,wedgePop,r= 81 0 239555.16012689518 1.2042\n",
      "12 yoyos for tract,wedge,wedgePop,r= 81 3 163983.53724604275 0.9202\n",
      "11 yoyos for tract,wedge,wedgePop,r= 81 0 221111.26720993183 1.1641\n",
      "13 yoyos for tract,wedge,wedgePop,r= 81 3 197870.76625593964 0.9361\n",
      "11 yoyos for tract,wedge,wedgePop,r= 81 0 197218.25612220747 1.144\n",
      "14 yoyos for tract,wedge,wedgePop,r= 81 3 184490.39430085837 0.9282\n",
      "12 yoyos for tract,wedge,wedgePop,r= 81 0 180457.39011660943 1.134\n",
      "15 yoyos for tract,wedge,wedgePop,r= 81 3 192389.75036019975 0.9321\n",
      "7 yoyos for tract,wedge,wedgePop,r= 94 1 456003.68524307857 1.4915\n",
      "8 yoyos for tract,wedge,wedgePop,r= 94 1 32587.35070882598 0.7457\n",
      "9 yoyos for tract,wedge,wedgePop,r= 94 1 490463.6575689711 1.6083\n",
      "10 yoyos for tract,wedge,wedgePop,r= 94 1 94452.34240796353 1.177\n",
      "11 yoyos for tract,wedge,wedgePop,r= 94 1 366015.3947494733 1.3926\n",
      "12 yoyos for tract,wedge,wedgePop,r= 94 1 157028.23243852047 1.2848\n",
      "12 yoyos for tract,wedge,wedgePop,r= 94 1 271725.354960079 1.3387\n",
      "7 yoyos for tract,wedge,wedgePop,r= 95 1 583640.1755827373 1.8531\n",
      "8 yoyos for tract,wedge,wedgePop,r= 95 1 53036.82605230913 0.9265\n",
      "9 yoyos for tract,wedge,wedgePop,r= 95 1 583600.2685173143 1.8529\n",
      "9 yoyos for tract,wedge,wedgePop,r= 95 1 317872.6018844192 1.3897\n",
      "10 yoyos for tract,wedge,wedgePop,r= 95 1 96842.57918808507 1.1581\n",
      "10 yoyos for tract,wedge,wedgePop,r= 95 1 115097.09617570043 1.2739\n",
      "10 yoyos for tract,wedge,wedgePop,r= 95 1 194923.4338888919 1.3318\n",
      "10 yoyos for tract,wedge,wedgePop,r= 95 1 258761.20585635246 1.3608\n",
      "10 yoyos for tract,wedge,wedgePop,r= 95 1 288071.2823766955 1.3752\n",
      "10 yoyos for tract,wedge,wedgePop,r= 95 1 303558.5882255858 1.3825\n",
      "we have 2 non-opposing shorted wedges for tract no 96\n",
      "7 yoyos for tract,wedge,wedgePop,r= 99 1 589075.7883772692 1.931\n",
      "8 yoyos for tract,wedge,wedgePop,r= 99 1 61420.71620225278 0.9655\n",
      "9 yoyos for tract,wedge,wedgePop,r= 99 1 589179.5828845508 1.9313\n",
      "9 yoyos for tract,wedge,wedgePop,r= 99 1 414906.7723180391 1.4484\n",
      "10 yoyos for tract,wedge,wedgePop,r= 99 1 106482.72048982308 1.207\n",
      "10 yoyos for tract,wedge,wedgePop,r= 99 1 200329.31323998034 1.3277\n",
      "I am working on tract number 100 of 1696 tracts\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 105 9.0 0 97.9 0.8999 192874.5\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 111 5.0 0 89.1 0.5678 192753.7\n",
      "I am working on tract number 120 of 1696 tracts\n",
      "I am working on tract number 140 of 1696 tracts\n",
      "I am working on tract number 160 of 1696 tracts\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 168 2.0 2 90.0 1.02 197356.2\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 168 3.0 2 90.0 0.9852 197356.2\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 168 4.0 2 90.0 0.9516 197356.2\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 168 5.0 2 90.0 0.9192 197356.2\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 168 6.0 2 90.0 0.8878 197356.2\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 168 7.0 2 90.0 0.8575 197356.2\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 168 8.0 2 90.0 0.8283 197356.2\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 168 9.0 2 90.0 0.8001 197356.2\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 168 10.0 2 90.0 0.7728 197356.2\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 168 11.0 2 90.0 0.7464 197356.2\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 168 12.0 2 90.0 0.721 197356.2\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 168 13.0 2 90.0 0.6964 197356.2\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 168 14.0 2 90.0 0.6727 197356.2\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 169 4.0 3 36.2 1.5626 675908.6\n",
      "we have 2 non-opposing shorted wedges for tract no 177\n",
      "I am working on tract number 180 of 1696 tracts\n",
      "I am working on tract number 200 of 1696 tracts\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 203 3.0 0 84.5 0.924 255949.3\n",
      "we have 2 non-opposing shorted wedges for tract no 217\n",
      "I am working on tract number 220 of 1696 tracts\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 224 3.0 0 109.5 0.8989 270501.6\n",
      "I am working on tract number 240 of 1696 tracts\n",
      "I am working on tract number 260 of 1696 tracts\n",
      "I am working on tract number 280 of 1696 tracts\n",
      "7 yoyos for tract,wedge,wedgePop,r= 287 1 282538.5951135063 1.2661\n",
      "8 yoyos for tract,wedge,wedgePop,r= 287 1 49016.32337913089 0.633\n",
      "9 yoyos for tract,wedge,wedgePop,r= 287 1 282469.13878823083 1.2626\n",
      "10 yoyos for tract,wedge,wedgePop,r= 287 1 108781.22824014397 0.9478\n",
      "11 yoyos for tract,wedge,wedgePop,r= 287 1 274788.13788336597 1.1052\n",
      "12 yoyos for tract,wedge,wedgePop,r= 287 1 143006.20130658662 1.0265\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 288 8.0 1 37.3 1.2787 275669.0\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 288 13.0 1 37.3 1.2747 275669.0\n",
      "7 yoyos for tract,wedge,wedgePop,r= 288 1 275668.97095214506 1.3621\n",
      "7 yoyos for tract,wedge,wedgePop,r= 290 3 374842.0948732232 1.778\n",
      "8 yoyos for tract,wedge,wedgePop,r= 290 3 116868.3881194618 0.889\n",
      "9 yoyos for tract,wedge,wedgePop,r= 290 3 374960.329261416 1.7789\n",
      "10 yoyos for tract,wedge,wedgePop,r= 290 3 180716.4494488963 1.334\n",
      "11 yoyos for tract,wedge,wedgePop,r= 290 3 342538.71794660995 1.5564\n",
      "11 yoyos for tract,wedge,wedgePop,r= 290 3 322401.2321624352 1.4452\n",
      "11 yoyos for tract,wedge,wedgePop,r= 290 3 260752.43683339303 1.3896\n",
      "12 yoyos for tract,wedge,wedgePop,r= 290 3 207508.58579583841 1.3618\n",
      "12 yoyos for tract,wedge,wedgePop,r= 290 3 226021.57706062385 1.3757\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 292 15.0 3 37.0 3.6804 368382.8\n",
      "I am working on tract number 300 of 1696 tracts\n",
      "we have 2 non-opposing shorted wedges for tract no 308\n",
      "we have 2 non-opposing shorted wedges for tract no 319\n",
      "I am working on tract number 320 of 1696 tracts\n",
      "I am working on tract number 340 of 1696 tracts\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 347 2.0 2 90.0 0.7565 225393.7\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 347 3.0 2 90.0 0.6595 225393.7\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 347 4.0 2 90.0 0.5749 225393.7\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 352 3.0 0 120.9 0.8659 189917.9\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 352 4.0 0 120.9 0.861 189917.9\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 352 5.0 0 120.9 0.8561 189917.9\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 352 6.0 0 120.9 0.8513 189917.9\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 352 7.0 0 120.9 0.8465 189917.9\n",
      "I am working on tract number 360 of 1696 tracts\n",
      "we have 2 non-opposing shorted wedges for tract no 366\n",
      "I am working on tract number 380 of 1696 tracts\n",
      "we have 2 non-opposing shorted wedges for tract no 398\n",
      "I am working on tract number 400 of 1696 tracts\n",
      "I am working on tract number 420 of 1696 tracts\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 421 2.0 0 90.0 0.8281 194234.2\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 421 3.0 0 90.0 0.8096 194234.2\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 421 4.0 0 90.0 0.7915 194234.2\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 421 5.0 0 90.0 0.7738 194234.2\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 421 6.0 0 90.0 0.7565 194234.2\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 421 7.0 0 90.0 0.7396 194234.2\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 421 8.0 0 90.0 0.723 194234.2\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 421 9.0 0 90.0 0.7069 194234.2\n",
      "we have 2 non-opposing shorted wedges for tract no 436\n",
      "I am working on tract number 440 of 1696 tracts\n",
      "I am working on tract number 460 of 1696 tracts\n",
      "I am working on tract number 480 of 1696 tracts\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 487 5.0 0 107.5 0.6814 495744.0\n",
      "I am working on tract number 500 of 1696 tracts\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 500 3.0 1 37.0 0.7582 500171.4\n",
      "7 yoyos for tract,wedge,wedgePop,r= 505 3 462443.1418590549 1.3617\n",
      "8 yoyos for tract,wedge,wedgePop,r= 505 3 97235.85193479207 0.6809\n",
      "9 yoyos for tract,wedge,wedgePop,r= 505 3 462415.98326390015 1.3614\n",
      "9 yoyos for tract,wedge,wedgePop,r= 505 3 336099.35785244545 1.0212\n",
      "I am working on tract number 520 of 1696 tracts\n",
      "I am working on tract number 540 of 1696 tracts\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 546 6.0 3 84.9 1.9395 253026.7\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 548 6.0 3 41.7 1.8833 384243.0\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 551 7.0 3 38.0 2.1116 342325.8\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 556 8.0 3 44.4 1.9745 357015.2\n",
      "I am working on tract number 560 of 1696 tracts\n",
      "we have 2 non-opposing shorted wedges for tract no 577\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 578 8.0 3 41.7 1.8779 454061.5\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 579 6.0 3 42.3 1.7267 281642.3\n",
      "I am working on tract number 580 of 1696 tracts\n",
      "I am working on tract number 600 of 1696 tracts\n",
      "I am working on tract number 620 of 1696 tracts\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 630 3.0 1 36.0 0.9164 320670.7\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 630 4.0 1 36.0 0.7208 320670.7\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 630 5.0 1 36.0 0.5669 320670.7\n",
      "I am working on tract number 640 of 1696 tracts\n",
      "I am working on tract number 660 of 1696 tracts\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 666 2.0 0 90.0 0.4582 193501.2\n",
      "I am working on tract number 680 of 1696 tracts\n",
      "I am working on tract number 700 of 1696 tracts\n",
      "I am working on tract number 720 of 1696 tracts\n",
      "I am working on tract number 740 of 1696 tracts\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 747 2.0 2 90.0 0.9855 216382.5\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 747 3.0 2 90.0 0.8869 216382.5\n",
      "7 yoyos for tract,wedge,wedgePop,r= 747 2 216382.51715154486 0.8198\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 747 18.0 0 101.6 0.9869 265207.3\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 747 19.0 0 101.6 0.8908 265207.3\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 747 20.0 0 101.6 0.8041 265207.3\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 747 21.0 0 101.6 0.7258 265207.3\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 749 3.0 2 90.0 0.9197 222340.4\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 749 7.0 1 70.9 0.8551 221642.2\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 749 8.0 1 70.9 0.8321 221642.2\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 749 9.0 1 70.9 0.8097 221642.2\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 749 10.0 1 70.9 0.788 221642.2\n",
      "we have 2 non-opposing shorted wedges for tract no 750\n",
      "I am working on tract number 760 of 1696 tracts\n",
      "we have 2 non-opposing shorted wedges for tract no 760\n",
      "we have 2 non-opposing shorted wedges for tract no 776\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 777 3.0 0 86.0 0.9791 206020.1\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 777 4.0 0 86.0 0.9153 206020.1\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 777 5.0 0 86.0 0.8555 206020.1\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 777 6.0 0 86.0 0.7997 206020.1\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 777 7.0 0 86.0 0.7475 206020.1\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 777 8.0 0 86.0 0.6988 206020.1\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 777 9.0 0 86.0 0.6532 206020.1\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 777 10.0 0 86.0 0.6105 206020.1\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 777 11.0 0 86.0 0.5707 206020.1\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 777 12.0 0 86.0 0.5335 206020.1\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 777 13.0 0 86.0 0.4987 206020.1\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 777 14.0 0 86.0 0.4661 206020.1\n",
      "I am working on tract number 780 of 1696 tracts\n",
      "we have 2 non-opposing shorted wedges for tract no 799\n",
      "I am working on tract number 800 of 1696 tracts\n",
      "we have 2 non-opposing shorted wedges for tract no 800\n",
      "we have 2 non-opposing shorted wedges for tract no 801\n",
      "we have 2 non-opposing shorted wedges for tract no 802\n",
      "we have 2 non-opposing shorted wedges for tract no 809\n",
      "we have 2 non-opposing shorted wedges for tract no 811\n",
      "7 yoyos for tract,wedge,wedgePop,r= 814 1 399933.2673067979 1.9165\n",
      "8 yoyos for tract,wedge,wedgePop,r= 814 1 119924.29025392723 0.9582\n",
      "9 yoyos for tract,wedge,wedgePop,r= 814 1 350858.0827006558 1.7376\n",
      "10 yoyos for tract,wedge,wedgePop,r= 814 1 261319.0081993442 1.3479\n",
      "11 yoyos for tract,wedge,wedgePop,r= 814 1 326548.085764244 1.5428\n",
      "11 yoyos for tract,wedge,wedgePop,r= 814 1 312958.8494678487 1.4454\n",
      "11 yoyos for tract,wedge,wedgePop,r= 814 1 299418.57215444255 1.3966\n",
      "11 yoyos for tract,wedge,wedgePop,r= 814 1 284520.3960956854 1.3723\n",
      "11 yoyos for tract,wedge,wedgePop,r= 814 1 273723.9685909959 1.3601\n",
      "I am working on tract number 820 of 1696 tracts\n",
      "we have 2 non-opposing shorted wedges for tract no 823\n",
      "we have 2 non-opposing shorted wedges for tract no 832\n",
      "we have 2 non-opposing shorted wedges for tract no 833\n",
      "we have 2 non-opposing shorted wedges for tract no 835\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 836 9.0 0 93.9 1.1156 204569.0\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 836 10.0 0 93.9 1.0485 204569.0\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 836 11.0 0 93.9 0.9854 204569.0\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 836 16.0 0 93.9 1.1828 204569.0\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 836 17.0 0 93.9 1.1117 204569.0\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 836 18.0 0 93.9 1.0448 204569.0\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 836 19.0 0 93.9 0.9819 204569.0\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 836 24.0 0 93.9 1.1854 204569.0\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 836 25.0 0 93.9 1.1141 204569.0\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 836 26.0 0 93.9 1.047 204569.0\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 836 27.0 0 93.9 0.984 204569.0\n",
      "loop31.0, tr836,wedgePops770219.04, 204569.0, 188827.6, 188699.7, 188122.6, Overedge?0, 0, 0, 0, ,Satisfied?0111,yoyo?6430 \n",
      "   targetWP, latest drx4 are tWP,dr, 188486.5,0.4601, 188486.5,0.0049, 188486.5,-0.0007, 188486.5,0.0073\n",
      "7 yoyos for tract,wedge,wedgePop,r= 836 0 204569.03010461616 1.2467\n",
      "loop32.0, tr836,wedgePops676494.02, 110844.0, 188827.6, 188699.7, 188122.6, Overedge?1, 0, 0, 0, ,Satisfied?0111,yoyo?0000 \n",
      "   targetWP, latest drx4 are tWP,dr, 214367.3,-0.6233, 214367.3,0.0049, 214367.3,-0.0007, 214367.3,0.0073\n",
      "loop33.0, tr836,wedgePops727572.51, 110844.0, 200360.0, 211210.4, 205158.0, Overedge?1, 0, 0, 0, ,Satisfied?0000,yoyo?0010 \n",
      "   targetWP, latest drx4 are tWP,dr, 214367.3,-0.6233, 214367.3,0.0268, 214367.3,0.0263, 214367.3,0.4635\n",
      "loop34.0, tr836,wedgePops747855.25, 110844.0, 206110.3, 213903.5, 216997.4, Overedge?1, 0, 0, 0, ,Satisfied?0000,yoyo?0010 \n",
      "   targetWP, latest drx4 are tWP,dr, 214367.3,-0.6233, 214367.3,0.0252, 214367.3,0.0029, 214367.3,0.1718\n",
      "loop35.0, tr836,wedgePops751524.68, 110844.0, 212044.7, 213903.5, 214732.5, Overedge?1, 0, 0, 0, ,Satisfied?0010,yoyo?0011 \n",
      "   targetWP, latest drx4 are tWP,dr, 214367.3,-0.6233, 214367.3,0.028, 214367.3,0.0029, 214367.3,-0.0297\n",
      "I am working on tract number 840 of 1696 tracts\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 840 5.0 0 86.0 0.5198 313329.8\n",
      "we have 2 non-opposing shorted wedges for tract no 843\n",
      "we have 2 non-opposing shorted wedges for tract no 844\n",
      "we have 2 non-opposing shorted wedges for tract no 845\n",
      "we have 2 non-opposing shorted wedges for tract no 846\n",
      "we have 2 non-opposing shorted wedges for tract no 848\n",
      "we have 2 non-opposing shorted wedges for tract no 849\n",
      "we have 2 non-opposing shorted wedges for tract no 851\n",
      "we have 2 non-opposing shorted wedges for tract no 852\n",
      "I am working on tract number 860 of 1696 tracts\n",
      "I am working on tract number 880 of 1696 tracts\n",
      "we have 2 non-opposing shorted wedges for tract no 894\n",
      "we have 2 non-opposing shorted wedges for tract no 896\n",
      "I am working on tract number 900 of 1696 tracts\n",
      "we have 2 non-opposing shorted wedges for tract no 901\n",
      "we have 2 non-opposing shorted wedges for tract no 902\n",
      "we have 2 non-opposing shorted wedges for tract no 908\n",
      "I am working on tract number 920 of 1696 tracts\n",
      "we have 2 non-opposing shorted wedges for tract no 926\n",
      "we have 2 non-opposing shorted wedges for tract no 927\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 934 2.0 0 90.0 0.7743 406211.5\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 934 5.0 0 97.4 0.7509 442014.7\n",
      "I am working on tract number 940 of 1696 tracts\n",
      "we have 2 non-opposing shorted wedges for tract no 951\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 957 2.0 0 90.0 0.8262 304900.2\n",
      "I am working on tract number 960 of 1696 tracts\n",
      "7 yoyos for tract,wedge,wedgePop,r= 965 3 485147.3993876014 0.9098\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 973 3.0 3 41.4 0.849 339190.4\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 973 4.0 3 41.4 0.5905 339190.4\n",
      "we have 2 non-opposing shorted wedges for tract no 977\n",
      "7 yoyos for tract,wedge,wedgePop,r= 977 0 494120.53477143304 1.823\n",
      "8 yoyos for tract,wedge,wedgePop,r= 977 0 316296.2781905647 0.9115\n",
      "9 yoyos for tract,wedge,wedgePop,r= 977 0 493756.3300465498 1.8172\n",
      "10 yoyos for tract,wedge,wedgePop,r= 977 0 358146.4906878106 1.3643\n",
      "11 yoyos for tract,wedge,wedgePop,r= 977 0 477205.33489025093 1.5907\n",
      "11 yoyos for tract,wedge,wedgePop,r= 977 0 461516.32672495744 1.4775\n",
      "11 yoyos for tract,wedge,wedgePop,r= 977 0 429933.1204897054 1.4209\n",
      "11 yoyos for tract,wedge,wedgePop,r= 977 0 393514.85973751335 1.3926\n",
      "11 yoyos for tract,wedge,wedgePop,r= 977 0 372749.98668431345 1.3785\n",
      "I am working on tract number 980 of 1696 tracts\n",
      "we have 2 non-opposing shorted wedges for tract no 980\n",
      "we have 2 non-opposing shorted wedges for tract no 983\n",
      "we have 2 non-opposing shorted wedges for tract no 984\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 996 3.0 0 116.6 0.9784 218501.3\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 996 3.0 3 63.4 0.8187 347598.4\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 996 4.0 0 116.6 0.8739 218501.3\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 996 5.0 0 116.6 0.7805 218501.3\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 996 6.0 0 116.6 0.6972 218501.3\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 996 7.0 0 116.6 0.6227 218501.3\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 996 8.0 0 116.6 0.5562 218501.3\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 996 9.0 0 116.6 0.4967 218501.3\n",
      "we have 2 non-opposing shorted wedges for tract no 998\n",
      "I am working on tract number 1000 of 1696 tracts\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 1000 3.0 3 36.4 0.9268 271368.9\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 1000 4.0 3 36.4 0.8715 271368.9\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 1000 5.0 3 36.4 0.8194 271368.9\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 1000 6.0 3 36.4 0.7705 271368.9\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 1000 7.0 3 36.4 0.7245 271368.9\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 1000 8.0 3 36.4 0.6813 271368.9\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 1000 9.0 3 36.4 0.6406 271368.9\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 1000 10.0 3 36.4 0.6023 271368.9\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 1000 11.0 3 36.4 0.5664 271368.9\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 1000 12.0 3 36.4 0.5326 271368.9\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 1000 13.0 3 36.4 0.5008 271368.9\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 1019 3.0 0 89.5 0.8893 191520.6\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 1019 4.0 0 89.5 0.8787 191520.6\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 1019 5.0 0 89.5 0.8682 191520.6\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 1019 6.0 0 89.5 0.8579 191520.6\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 1019 7.0 0 89.5 0.8476 191520.6\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 1019 8.0 0 89.5 0.8375 191520.6\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 1019 8.0 3 90.5 1.7435 259835.3\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 1019 9.0 0 89.5 0.8275 191520.6\n",
      "I am working on tract number 1020 of 1696 tracts\n",
      "I am working on tract number 1040 of 1696 tracts\n",
      "7 yoyos for tract,wedge,wedgePop,r= 1051 1 538716.7795651502 1.2638\n",
      "8 yoyos for tract,wedge,wedgePop,r= 1051 1 40510.81558313465 0.6319\n",
      "9 yoyos for tract,wedge,wedgePop,r= 1051 1 530176.7842398904 1.2246\n",
      "9 yoyos for tract,wedge,wedgePop,r= 1051 1 412362.3054357449 0.9283\n",
      "10 yoyos for tract,wedge,wedgePop,r= 1051 1 200788.46078317563 0.7801\n",
      "11 yoyos for tract,wedge,wedgePop,r= 1051 1 332823.6616306477 0.8542\n",
      "11 yoyos for tract,wedge,wedgePop,r= 1051 1 273925.98328632856 0.8171\n",
      "11 yoyos for tract,wedge,wedgePop,r= 1051 1 241145.10769791063 0.7986\n",
      "11 yoyos for tract,wedge,wedgePop,r= 1051 1 221073.40435839532 0.7893\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 1052 2.0 1 90.0 0.9197 218172.9\n",
      "I am working on tract number 1060 of 1696 tracts\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 1066 3.0 3 37.8 0.828 439607.1\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 1068 4.0 3 36.6 0.8095 486895.8\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 1070 3.0 1 46.8 1.013 201238.3\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 1070 4.0 0 133.2 1.8165 241270.0\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 1070 4.0 1 46.8 0.964 201238.3\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 1070 5.0 1 46.8 0.9175 201238.3\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 1073 4.0 0 86.6 1.9253 204296.2\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 1073 5.0 0 86.6 1.8851 204296.2\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 1073 6.0 0 86.6 1.8457 204296.2\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 1073 7.0 0 86.6 1.8072 204296.2\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 1073 8.0 0 86.6 1.7695 204296.2\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 1073 9.0 0 86.6 1.7325 204296.2\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 1073 10.0 0 86.6 1.6964 204296.2\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 1073 11.0 0 86.6 1.661 204296.2\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 1073 12.0 0 86.6 1.6263 204296.2\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 1073 13.0 0 86.6 1.5923 204296.2\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 1073 14.0 0 86.6 1.5591 204296.2\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 1073 15.0 0 86.6 1.5265 204296.2\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 1073 16.0 0 86.6 1.4947 204296.2\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 1073 17.0 0 86.6 1.4635 204296.2\n",
      "I am working on tract number 1080 of 1696 tracts\n",
      "I am working on tract number 1100 of 1696 tracts\n",
      "we have 2 non-opposing shorted wedges for tract no 1114\n",
      "I am working on tract number 1120 of 1696 tracts\n",
      "we have 2 non-opposing shorted wedges for tract no 1127\n",
      "I am working on tract number 1140 of 1696 tracts\n",
      "I am working on tract number 1160 of 1696 tracts\n",
      "I am working on tract number 1180 of 1696 tracts\n",
      "I am working on tract number 1200 of 1696 tracts\n",
      "I am working on tract number 1220 of 1696 tracts\n",
      "I am working on tract number 1240 of 1696 tracts\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 1252 2.0 1 90.0 0.8161 257494.4\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 1252 3.0 1 90.0 0.6393 257494.4\n",
      "I am working on tract number 1260 of 1696 tracts\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 1260 2.0 3 90.0 0.9011 199518.3\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 1260 3.0 3 90.0 0.8632 199518.3\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 1260 4.0 3 90.0 0.8269 199518.3\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 1260 5.0 3 90.0 0.7921 199518.3\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 1260 6.0 3 90.0 0.7588 199518.3\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 1260 7.0 3 90.0 0.7269 199518.3\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 1260 8.0 3 90.0 0.6963 199518.3\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 1260 9.0 3 90.0 0.667 199518.3\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 1260 10.0 3 90.0 0.6389 199518.3\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 1260 11.0 3 90.0 0.6121 199518.3\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 1260 12.0 3 90.0 0.5863 199518.3\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 1260 13.0 3 90.0 0.5617 199518.3\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 1260 14.0 3 90.0 0.538 199518.3\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 1260 15.0 3 90.0 0.5154 199518.3\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 1263 2.0 0 90.0 0.9899 210242.6\n",
      "we have 2 non-opposing shorted wedges for tract no 1268\n",
      "we have 2 non-opposing shorted wedges for tract no 1269\n",
      "we have 2 non-opposing shorted wedges for tract no 1271\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 1276 5.0 0 89.4 0.4166 191713.4\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 1276 6.0 0 89.4 0.4114 191713.4\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 1276 7.0 0 89.4 0.4061 191713.4\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 1276 8.0 0 89.4 0.401 191713.4\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 1276 9.0 0 89.4 0.3959 191713.4\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 1276 10.0 0 89.4 0.3909 191713.4\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 1276 11.0 0 89.4 0.3859 191713.4\n",
      "I am working on tract number 1280 of 1696 tracts\n",
      "I am working on tract number 1300 of 1696 tracts\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 1311 7.0 0 121.7 1.4881 217453.1\n",
      "7 yoyos for tract,wedge,wedgePop,r= 1311 0 217349.47868145182 1.38\n",
      "8 yoyos for tract,wedge,wedgePop,r= 1311 0 47075.418203872425 0.69\n",
      "9 yoyos for tract,wedge,wedgePop,r= 1311 0 217354.95260505995 1.3818\n",
      "10 yoyos for tract,wedge,wedgePop,r= 1311 0 90506.42089766994 1.0359\n",
      "10 yoyos for tract,wedge,wedgePop,r= 1311 0 158435.45244854531 1.2089\n",
      "11 yoyos for tract,wedge,wedgePop,r= 1311 0 217248.768969581 1.2954\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 1315 4.0 0 83.4 0.82 195168.9\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 1315 5.0 0 83.4 0.7988 195168.9\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 1315 6.0 0 83.4 0.7781 195168.9\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 1315 7.0 0 83.4 0.7579 195168.9\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 1315 8.0 0 83.4 0.7383 195168.9\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 1315 9.0 0 83.4 0.7192 195168.9\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 1315 10.0 0 83.4 0.7005 195168.9\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 1315 11.0 0 83.4 0.6824 195168.9\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 1315 12.0 0 83.4 0.6647 195168.9\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 1315 13.0 0 83.4 0.6475 195168.9\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 1315 14.0 0 83.4 0.6307 195168.9\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 1315 15.0 0 83.4 0.6144 195168.9\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 1315 16.0 0 83.4 0.5985 195168.9\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 1315 17.0 0 83.4 0.583 195168.9\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 1315 18.0 0 83.4 0.5679 195168.9\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 1315 19.0 0 83.4 0.5532 195168.9\n",
      "I am working on tract number 1320 of 1696 tracts\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 1322 3.0 1 90.0 0.5993 320562.6\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 1332 3.0 1 36.8 0.45 287305.4\n",
      "I am working on tract number 1340 of 1696 tracts\n",
      "I am working on tract number 1360 of 1696 tracts\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 1371 6.0 0 94.9 0.8647 204919.5\n",
      "we have 2 non-opposing shorted wedges for tract no 1377\n",
      "we have 2 non-opposing shorted wedges for tract no 1378\n",
      "I am working on tract number 1380 of 1696 tracts\n",
      "I am working on tract number 1400 of 1696 tracts\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 1402 3.0 3 49.8 0.3939 218101.9\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 1402 4.0 3 49.8 0.3895 218101.9\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 1402 5.0 3 49.8 0.3851 218101.9\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 1402 6.0 3 49.8 0.3808 218101.9\n",
      "we have 2 non-opposing shorted wedges for tract no 1407\n",
      "we have 2 non-opposing shorted wedges for tract no 1411\n",
      "we have 2 non-opposing shorted wedges for tract no 1412\n",
      "I am working on tract number 1420 of 1696 tracts\n",
      "we have 2 non-opposing shorted wedges for tract no 1428\n",
      "we have 2 non-opposing shorted wedges for tract no 1434\n",
      "I am working on tract number 1440 of 1696 tracts\n",
      "we have 2 non-opposing shorted wedges for tract no 1442\n",
      "we have 2 non-opposing shorted wedges for tract no 1455\n",
      "I am working on tract number 1460 of 1696 tracts\n",
      "we have 2 non-opposing shorted wedges for tract no 1471\n",
      "we have 2 non-opposing shorted wedges for tract no 1472\n",
      "we have 2 non-opposing shorted wedges for tract no 1477\n",
      "I am working on tract number 1480 of 1696 tracts\n",
      "we have 2 non-opposing shorted wedges for tract no 1483\n",
      "I am working on tract number 1500 of 1696 tracts\n",
      "we have 2 non-opposing shorted wedges for tract no 1506\n",
      "we have 2 non-opposing shorted wedges for tract no 1507\n",
      "I am working on tract number 1520 of 1696 tracts\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 1520 5.0 0 86.4 0.6099 205418.6\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 1520 6.0 0 86.4 0.5714 205418.6\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 1524 2.0 0 90.0 0.6497 253952.1\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 1524 5.0 0 83.6 0.6708 235766.0\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 1524 6.0 0 83.6 0.5643 235766.0\n",
      "I am working on tract number 1540 of 1696 tracts\n",
      "7 yoyos for tract,wedge,wedgePop,r= 1545 3 574400.2285210541 1.6328\n",
      "8 yoyos for tract,wedge,wedgePop,r= 1545 3 9979.337983731879 0.8164\n",
      "9 yoyos for tract,wedge,wedgePop,r= 1545 3 698406.5250507261 1.758\n",
      "10 yoyos for tract,wedge,wedgePop,r= 1545 3 48175.650455003255 1.2872\n",
      "11 yoyos for tract,wedge,wedgePop,r= 1545 3 262715.63358471927 1.5226\n",
      "12 yoyos for tract,wedge,wedgePop,r= 1545 3 87850.40535882532 1.4049\n",
      "12 yoyos for tract,wedge,wedgePop,r= 1545 3 149471.8645161211 1.4638\n",
      "12 yoyos for tract,wedge,wedgePop,r= 1545 3 194255.4355175114 1.4932\n",
      "12 yoyos for tract,wedge,wedgePop,r= 1545 3 226367.42031118716 1.5079\n",
      "12 yoyos for tract,wedge,wedgePop,r= 1545 3 245406.76134787855 1.5153\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 1559 4.0 0 112.7 0.9297 232604.9\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 1559 4.0 3 67.3 0.8991 253448.3\n",
      "I am working on tract number 1560 of 1696 tracts\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 1564 3.0 0 110.2 0.9565 230470.0\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 1564 4.0 0 110.2 0.8193 230470.0\n",
      "we have 2 non-opposing shorted wedges for tract no 1566\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 1567 5.0 0 133.0 0.705 261254.4\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 1568 4.0 3 51.8 0.8119 359619.4\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 1579 2.0 2 90.0 0.9409 209261.2\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 1579 3.0 2 90.0 0.869 209261.2\n",
      "I am working on tract number 1580 of 1696 tracts\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 1580 3.0 3 90.0 0.8405 341665.9\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 1580 7.0 0 85.3 1.0208 196786.8\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 1580 8.0 0 85.3 0.9881 196786.8\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 1580 9.0 0 85.3 0.9565 196786.8\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 1580 10.0 0 85.3 0.926 196786.8\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 1580 11.0 0 85.3 0.8963 196786.8\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 1580 12.0 0 85.3 0.8677 196786.8\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 1580 13.0 0 85.3 0.8399 196786.8\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 1580 14.0 0 85.3 0.8131 196786.8\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 1580 15.0 0 85.3 0.7871 196786.8\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 1580 16.0 0 85.3 0.7619 196786.8\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 1580 17.0 0 85.3 0.7376 196786.8\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 1580 18.0 0 85.3 0.714 196786.8\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 1580 19.0 0 85.3 0.6911 196786.8\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 1580 20.0 0 85.3 0.669 196786.8\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 1580 21.0 0 85.3 0.6477 196786.8\n",
      "I am working on tract number 1600 of 1696 tracts\n",
      "we have 2 non-opposing shorted wedges for tract no 1601\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 1610 5.0 0 102.3 1.1738 208179.7\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 1610 6.0 0 102.3 1.0884 208179.7\n",
      "7 yoyos for tract,wedge,wedgePop,r= 1618 1 295173.1792790803 1.4084\n",
      "8 yoyos for tract,wedge,wedgePop,r= 1618 1 51464.76478163243 0.7042\n",
      "9 yoyos for tract,wedge,wedgePop,r= 1618 1 294914.9962842168 1.4074\n",
      "10 yoyos for tract,wedge,wedgePop,r= 1618 1 139798.58990392383 1.0558\n",
      "11 yoyos for tract,wedge,wedgePop,r= 1618 1 272758.8572502837 1.2316\n",
      "11 yoyos for tract,wedge,wedgePop,r= 1618 1 228396.30124401546 1.1437\n",
      "12 yoyos for tract,wedge,wedgePop,r= 1618 1 182493.4935513303 1.0998\n",
      "13 yoyos for tract,wedge,wedgePop,r= 1618 1 209796.06969359 1.1217\n",
      "13 yoyos for tract,wedge,wedgePop,r= 1618 1 195695.93868408614 1.1107\n",
      "I am working on tract number 1620 of 1696 tracts\n",
      "7 yoyos for tract,wedge,wedgePop,r= 1621 1 649885.476962016 2.3151\n",
      "8 yoyos for tract,wedge,wedgePop,r= 1621 1 91871.64410449017 1.1576\n",
      "9 yoyos for tract,wedge,wedgePop,r= 1621 1 659417.8556149275 2.3391\n",
      "10 yoyos for tract,wedge,wedgePop,r= 1621 1 176380.93955561577 1.7483\n",
      "11 yoyos for tract,wedge,wedgePop,r= 1621 1 518117.94129606395 2.0437\n",
      "12 yoyos for tract,wedge,wedgePop,r= 1621 1 247067.4087395522 1.896\n",
      "13 yoyos for tract,wedge,wedgePop,r= 1621 1 409083.27798595 1.9698\n",
      "14 yoyos for tract,wedge,wedgePop,r= 1621 1 330670.71497588017 1.9329\n",
      "15 yoyos for tract,wedge,wedgePop,r= 1621 1 370088.0161500789 1.9514\n",
      "16 yoyos for tract,wedge,wedgePop,r= 1621 1 348740.4549853488 1.9422\n",
      "we have 2 non-opposing shorted wedges for tract no 1622\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 1631 10.0 0 131.6 2.5593 255706.9\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 1631 11.0 0 131.6 2.4447 255706.9\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 1631 12.0 0 131.6 2.3352 255706.9\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 1631 13.0 0 131.6 2.2307 255706.9\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 1631 14.0 0 131.6 2.1308 255706.9\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 1631 19.0 0 131.6 2.5135 255706.9\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 1631 20.0 0 131.6 2.4009 255706.9\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 1631 21.0 0 131.6 2.2934 255706.9\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 1631 22.0 0 131.6 2.1907 255706.9\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 1631 23.0 0 131.6 2.0927 255706.9\n",
      "7 yoyos for tract,wedge,wedgePop,r= 1631 0 255706.90026168662 2.6678\n",
      "7 yoyos for tract,wedge,wedgePop,r= 1638 3 769165.5890559751 2.0825\n",
      "I am working on tract number 1640 of 1696 tracts\n",
      "I am working on tract number 1660 of 1696 tracts\n",
      "I am working on tract number 1680 of 1696 tracts\n",
      "we have 2 non-opposing shorted wedges for tract no 1686\n",
      "we have 2 non-opposing shorted wedges for tract no 1689\n",
      "we have 2 non-opposing shorted wedges for tract no 1695\n",
      "Sum of weights over all precinct home districts should have been 1.000, but was  1.000070852627229\n",
      "Average and max number of wedgePop loops per tract were:  7.181603773584905 35.0\n",
      "min,average,max of Home District areas were:  0.0 1.100514010510079 2.2882667835172055\n",
      "calculated statewide vote was 0.5902247784374044, should have been 0.5819819637963837\n",
      "calcd statewide Hispanic pop was 0.05767658981521857, should have been 0.06758106135698658\n",
      "calcd statewide Black pop was 0.07632694734168953, should have been 0.09009954398382769\n",
      "fraction of HDs that with altered wedge angles near boundaries =  0.46403301886792453\n"
     ]
    }
   ],
   "source": [
    "#3/13/22 - from HD2, but using coeff1, coeff2 (unrestricted opp wedge pop, wideAngle quadratic in ratio)\n",
    "# Bisect for yoyo > 3, precinct calcs after wedges finalized.  More aggressive Euler gain\n",
    "#minTractPop = 10  #this is set in a prior block\n",
    "pi=3.1415926536\n",
    "nWedges = 4  #number of wedges per home district polygon  \n",
    "avgWedgeAngle = 2*pi / nWedges\n",
    "wedgeTriAR = [math.sin(pi/nWedges)*math.cos(pi/nWedges)]*nWedges  #wedge triangle area ratio (= area / r*r)\n",
    "angle = [0.]*nWedges\n",
    "angle2 = [0.]*nWedges\n",
    "pt1 = [Point(0,0)]*nWedges  #these four define the polygon wedge for a growing home district\n",
    "pt2 = [Point(0,0)]*nWedges\n",
    "pt3 = [Point(0,0)]*nWedges\n",
    "pt4 = [Point(0,0)]*nWedges\n",
    "oldR = [0.]*nWedges  #wedge radius from previous loop\n",
    "# guessedR = [0.]*nWedges  #obsolete; was wedge radius if we extrapolate from population density of most recent wedge piece\n",
    "printPoly = [Polygon([(0,0),(0,1),(1,1)])]*nWedges #for debugging\n",
    "wedgePop = [0.]*nWedges\n",
    "tractLoopCounter = [0.]* nTracts  #this tracks how many loops per tract to get to target wedge Pop\n",
    "nearEdge = [0.]* nTracts   #not yet implemented; this will flag tracts near map edge for reprocessing\n",
    "tractUse = [0.] * nTracts  #this will store how much we use this tract in ALL HD's vs. expectation\n",
    "precinctUse = [0.] * nPrecincts   #same, but for each precinct / VTD\n",
    "loopTractUse = [0.] * nTracts  #same as above two, but only for the current loop / tract\n",
    "loopPrecinctUse = [0.] * nPrecincts\n",
    "HDvPop = [0.]*nTracts\n",
    "HDvGOP = [0.]*nTracts  #GOP lean of each tract's \"home district\"\n",
    "HDvBlack = [0.]*nTracts\n",
    "HDvHisp = [0.]*nTracts #pct Hispanic by home district\n",
    "HDweight = [0.]*nTracts  #relative weight of each district.  In absence of splits, will equal precinct pop\n",
    "HDarea = [0.]*nTracts  #geographical area of each home district\n",
    "HDradius = [[0.]*nWedges for t in range(nTracts)] #final wedge length for each Home District wedge\n",
    "HDangle = [[0.]*nWedges for t in range(nTracts)] #final included angle for each HD wedge\n",
    "angle0 = [-999.] * nTracts  #orientation of 0th wedge.  Random except re-oriented if we are near-boundary\n",
    "toler = 0.005  #adjustable - fractional slop in district population.  normally 0.01, to be reduced to 0.005\n",
    "tolerPop = toler * avgDistrictPop  #absolute slop in district pop\n",
    "nLoopPrint = 30  #at this loop number, we alert user of problem\n",
    "nGiveUp = 40  #we punt after this many loops\n",
    "nLoopSuperPrint = nGiveUp - 2 # at this loop number, we output wedge growth visuals\n",
    "wrongPop = [0.]*nTracts\n",
    "normalGain = 0.8  #adjustable - a bit less than 1.0 for stability, how far we step relative to expected perfect guess\n",
    "EulerGain = 1.5 #if we hit empty land, gain more aggressively to get through it\n",
    "#yoyoFactor = 0.3 #rate of reduction in gain as solver continues to yoyo in solving a wedge  #not currently used ***\n",
    "globalMax_dr = (MAP.area/nDistricts) ** 0.5  #put a reasonable upper limit on wedge radial growth step size\n",
    "tractPrintInterval = 20  #for tracking progress\n",
    "minTractArea = 0.00001 * MAP.area / nTracts  #failsafe for later div-by-zero\n",
    "homePopDensity = avgGridDensity  #seed this\n",
    "didWeRestart = [0] * nTracts  #will flag if we adjusted wedge angles due to a single shorted wedge\n",
    "coeff1 = 0.3  #linear term. we will adjust this empirically to tighten tract weighting near boundaries\n",
    "coeff2 = 0.3  #quadratic term.  These two control distortion in wedge angles relative to wedgePop shortness\n",
    "minWedgePop = [avgDistrictPop/nWedges] * nTracts  #wedge pop for wedge facing boundary (for tracts near boundaries)\n",
    "maxWideAngle = 0.8 * pi  #adjustable parameter.  (avoid wedges too close to half a pie\n",
    "\n",
    "for t in range(nTracts) :  #(nTracts):  #loop on each tract.  Start by resetting stats\n",
    "    gain = normalGain  #in case last precinct had convergence problems\n",
    "    if (t % tractPrintInterval) == 0 : \n",
    "        print(\"I am working on tract number {0} of {1} tracts\".format(t,nTracts) )\n",
    "    tractLoopCounter[t] = 0.  #for stability stats, will track how many times we need to loop for each tract\n",
    "    # isActiveG = [0]*nGrids  #resets whether each grid is relevant to this tract (not used; see alt grid turn-on method)\n",
    "    if (tractArea[t] > minTractArea) :  #too lazy to indent everything.  We'll catch zero tractArea later\n",
    "        homePopDensity = tractPop[t]/tractArea[t]  #temporary - estimate the local density for initial step\n",
    "    tractCP = Point(tractGeom[t].centroid.x,tractGeom[t].centroid.y)  #shorthand for centroid of each tract\n",
    "    for nG in range(nGrids):\n",
    "        if gridGeom[nG].contains(tractCP) :  #which grid contains the centroid of this tract?  Turn it on!\n",
    "            # isActiveG[nG] = 1  #I don't think I use this\n",
    "            homeGridDensity = gridDensity[nG]\n",
    "    maxStartDensity = max(homePopDensity,homeGridDensity)\n",
    "    minR = (avgDistrictPop / maxStartDensity / pi)**0.5   # conservative estimated radius of this tract's district\n",
    "    tinyR = 0.01 * minR   #ensures we start with small, but not infinitesimal wedge populations\n",
    "    angle0[t] = random.uniform(0,2.*pi)  #imparts random orientation to our starting wedge\n",
    "    loopTractUse = [0.]*nTracts  #this and below help us reset tract n precinct use after a wedge reset\n",
    "    loopPrecinctUse = [0.]*nPrecincts\n",
    "    # ***GROW WEDGES SIMULTANEOUSLY via ARRAYS, SO WE CAN REACT ON FLY TO BOUNDARY STOPS\n",
    "    HDpop = 0.  #running total of this tract's \"home district\" population\n",
    "    nActiveWedges = nWedges #active wedges have not run over boudnary\n",
    "    targetWedgePop = [avgDistrictPop / nWedges]*nWedges  #at beginning, split districtPop equally among wedges\n",
    "    #latestWedgeDensity = [0.]*nWedges  #stop using # pop'n density of the wedge piece we just added or subtracted\n",
    "    wedgePop = [0.]*nWedges\n",
    "    oldWedgePop = [0.]*nWedges  #wedge pop from previous round (needed for NR projection)\n",
    "    wedgePopGap = [0.]*nWedges\n",
    "    isOverEdge = [0] *nWedges #each wedge is still fully inside map boundary\n",
    "    isSatisfied = [0] * nWedges #wedges close enough to target are not improved upon\n",
    "    yoyoCount = [0] * nWedges #tracks how many times we've reversed this wedge's growth vs. shrink at current wPop target\n",
    "    wedgeMaxR = [0.] * nWedges #will track the largest radius this wedge has seen (to cap yoyo bisection step)\n",
    "    wedgeAngle = [avgWedgeAngle] * nWedges #reset to equi-angle when starting each tract    \n",
    "    currentR = [0.]*nWedges\n",
    "    old_dr = [0.]*nWedges\n",
    "    wedgeStop = 0  #obsolete? this will flag if we need to rejigger oldR when we stopped an over-boundary wedge\n",
    "    for nW in range(nWedges):\n",
    "        currentR[nW] = tinyR  #each wedge starts as a tiny triangle of this radius\n",
    "        old_dr[nW] = currentR[nW] #this will track the last radial step (to help convergence)\n",
    "    oldR = [0.]*nWedges   #for remembering last loop's wedge radius\n",
    "    max_dr = [globalMax_dr]*nWedges  #reset max possible positive radial step size\n",
    "    for nW in range(nWedges) :  #this loop: set up tiny wedges in each direction to seed each wedge loop\n",
    "        angle[nW] = (nW-0.5)*wedgeAngle[nW]+angle0[t]   #local variable for orientation of START of wedge\n",
    "        angle2[nW] = angle[nW]+wedgeAngle[nW]      #local angle for clockwise END of wedge\n",
    "        pt1[nW] = tractCP\n",
    "        pt2[nW] = Point(tractCP.x+currentR[nW]*math.cos(angle[nW]),  tractCP.y+currentR[nW]*math.sin(angle[nW]) )\n",
    "        pt3[nW] = Point(tractCP.x+currentR[nW]*math.cos(angle2[nW]), tractCP.y+currentR[nW]*math.sin(angle2[nW]) )\n",
    "        wedgePoly = Polygon([pt1[nW], pt2[nW], pt3[nW] ]) #build a tiny starter wedge triangle\n",
    "        wedgePop[nW] = tractPop[t]* wedgePoly.area/max(tractArea[t],minTractArea)  #max prevents rare div-by-zero\n",
    "    HDpop = np.sum(wedgePop)\n",
    "    if (tractPop[t] < minTractPop or tractArea[t] == 0 or isSkippedTract[t] == 1):\n",
    "        #go directly to jail, do not pass Go, this tract doesn't count\n",
    "        HDpop = avgDistrictPop  #white lie to kick us out of loop\n",
    "    while abs (HDpop - avgDistrictPop) > tolerPop :  #until we've grown the home district to the right size...\n",
    "        sumWedgePopGapChange = 0  #this will total wedge pops that fall short of expectations (when over boundary)\n",
    "        for nW in range(nWedges) :  #for each wedge, we'll build to gain pop or shrink to lose population ...\n",
    "            neededWedgePop = targetWedgePop[nW] - wedgePop[nW]\n",
    "            isSatisfied[nW] = 0  #default in case target changed last loop.  We'll check immediately below\n",
    "            if abs(neededWedgePop/targetWedgePop[nW]) < 0.5*toler :  #is this wedge close enough to stop iterating on it?\n",
    "                isSatisfied[nW] = 1\n",
    "            if isOverEdge[nW] == 0 and isSatisfied[nW] == 0:   #we skip over-boundary and near-perfect wedges\n",
    "                wedgePopDelta = wedgePop[nW] - oldWedgePop[nW]  #how much wedgePop gained in last loop\n",
    "                if (wedgePopDelta == 0): #our last wedge change was in an empty area (desert or off map)\n",
    "                    gainAdjust = EulerGain/gain  #Euler method often over-cautious at map edges or in sparse areas\n",
    "                    guessedRsquared = currentR[nW]*currentR[nW] * targetWedgePop[nW] / wedgePop[nW]  #Euler guess\n",
    "                    # can't use Newton-Raphson since last dy was zero, use Euler instead\n",
    "                    print(\"we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop\",t,tractLoopCounter[t],\n",
    "                          nW, round(180./pi*wedgeAngle[nW],1),round(currentR[nW],4),round(wedgePop[nW],1))\n",
    "                else: #use N-R estimation\n",
    "                    gainAdjust = 1.0\n",
    "                    R2delta = currentR[nW]*currentR[nW] - oldR[nW]*oldR[nW]  #this and below are run/rise of current slope\n",
    "                    guessedRsquared = currentR[nW]*currentR[nW] + R2delta / wedgePopDelta * neededWedgePop\n",
    "                    \n",
    "                guessedRsquared = max( guessedRsquared, 0. ) #minimum guardrail\n",
    "                guessed_dr = guessedRsquared ** 0.5 - currentR[nW]  #best guess for wedge radius change\n",
    "                #gain = max(0.3,normalGain ** (1. + yoyoFactor * yoyoCount[nW]))  #obsolete; using bisect\n",
    "                dr = gain * gainAdjust * guessed_dr   #gain typically ~0.8 for stability\n",
    "                dr = max( -0.5*currentR[nW],min(dr,max_dr[nW]) )  #apply min and max guardrails to wedge radius change\n",
    "                \n",
    "                if np.sign(dr) != np.sign(old_dr[nW]) :  #are we yoyoing? How many times?\n",
    "                    yoyoCount[nW] += 1\n",
    "                if yoyoCount[nW] > 3 and yoyoCount[nW] <= 6 :\n",
    "                    wedgeMaxR[nW] = max(wedgeMaxR[nW],currentR[nW]) #will track largest R in yoyo cycle\n",
    "                    max_dr[nW] = 2.*wedgeMaxR[nW]  #this will now store a reasonable max bisection step size\n",
    "                if yoyoCount[nW] > 6 :  #switch to bisection method; too many yo-yo's\n",
    "                    print(yoyoCount[nW],\"yoyos for tract,wedge,wedgePop,r=\",t,nW,wedgePop[nW],round(currentR[nW],4) )\n",
    "                    max_dr[nW] = 0.5 * max_dr[nW] #we are now bisecting ...\n",
    "                    if(neededWedgePop < 0):\n",
    "                        dr = max(-1.*max_dr[nW], -0.5*currentR[nW])  #reduce wedge size\n",
    "                    else:\n",
    "                        dr = max_dr[nW]  #increase wedge size\n",
    "                old_dr[nW] = dr  #these three lines -- save current as old values for next loop\n",
    "                oldR[nW] = currentR[nW]\n",
    "                oldWedgePop[nW] = wedgePop[nW]\n",
    "                if dr > 0. :  #we are growing a wedge trapezoid piece \n",
    "                    outerR = currentR[nW] + dr\n",
    "                    innerR = currentR[nW]\n",
    "                    currentR[nW] = outerR    #for next loop around\n",
    "                else:    #this wedge trapezoid piece will be SUBTRACTED from current wedge\n",
    "                    outerR = currentR[nW]\n",
    "                    innerR = currentR[nW] + dr  #remember, dr is negative here\n",
    "                    currentR[nW] = innerR             #for next loop around\n",
    "                #now, describe the new wedge to probe for precinct intersections ...\n",
    "                pt1[nW] = Point(tractCP.x+innerR*math.cos(angle[nW]), tractCP.y+innerR*math.sin(angle[nW]) )\n",
    "                pt2[nW] = Point(tractCP.x+outerR*math.cos(angle[nW]), tractCP.y+outerR*math.sin(angle[nW]) )\n",
    "                pt3[nW] = Point(tractCP.x+outerR*math.cos(angle2[nW]),tractCP.y+outerR*math.sin(angle2[nW]) )\n",
    "                pt4[nW] = Point(tractCP.x+innerR*math.cos(angle2[nW]),tractCP.y+innerR*math.sin(angle2[nW]) )\n",
    "                wedgePoly = Polygon([pt1[nW], pt2[nW], pt3[nW],pt4[nW]])  #true for wedge add-on or to-be-trimmed\n",
    "                \n",
    "                printPoly[nW] = wedgePoly  #for debugging\n",
    "                latestWedgePop = 0.  #for the new piece, not the entire triangle\n",
    "                usedTract = [0]*nTracts  #rezero lists of tracts and precincts that could straddle multiple grids\n",
    "                usedPrecinct = [0]*nPrecincts\n",
    "                for nG in range(nGrids) :  # loop over ACTIVE grids to look for intersecting tracts\n",
    "                    gridIntersxn = gridGeom[nG].intersection(wedgePoly)\n",
    "                    if (gridIntersxn.area > 0) :  #this grid is RELEVANT to this wedge\n",
    "                        for tt in range(nGridTracts[nG]) : #look for intersxns with all tracts in this grid\n",
    "                            nTT = gridTractNo[nG][tt] #shorthand for this tract's global tract no\n",
    "                            if(usedTract[nTT] == 0) :  #Did we not already look at this tract in another grid list?\n",
    "                                usedTract[nTT] = 1  #well, now we have!  Probe intersection with wedge\n",
    "                                overlap = tractGeom[nTT].intersection(wedgePoly).area\n",
    "                                if overlap > 0 :\n",
    "                                    fracArea = overlap/tractArea[nTT]\n",
    "                                    latestWedgePop  += fracArea*tractPop[nTT]  #always positive (used in density calc)\n",
    "                                    loopTractUse[nTT] += np.sign(dr)*overlap/tractArea[nTT] * tractPop[t]/avgDistrictPop\n",
    "                        # found all possible tract overlaps with this increment / decrement to this wedge.\n",
    "                        \n",
    "                wedgePop[nW] += np.sign(dr)*latestWedgePop  #for full triangle, based on this latest piece\n",
    "                # Now, flag if we're beyond boundary\n",
    "                if wedgePop[nW] < targetWedgePop[nW] : #if growing, confirm we're not beyond MAP boundary\n",
    "                    leadingEdge = LineString([pt2[nW],pt3[nW]])\n",
    "                    if leadingEdge.intersects(MAP) :  #still room to grow in part of edge, keep going\n",
    "                        gerrymandering = \"evil\"  #it had to be said\n",
    "                    else:  #this wedge is fully beyond the map. give up on more map intersection\n",
    "                        isOverEdge[nW] = 1\n",
    "                        shortedWedge = nW  #ID'ing highest numbered wedge that got shorted by the boundary in this loop\n",
    "                        oldWedgePopGap = wedgePopGap[nW]\n",
    "                        wedgePopGap[nW] = targetWedgePop[nW] - wedgePop[nW]  #how far this wedge's pop is below its target\n",
    "                        nActiveWedges -= 1  #a few lines below, we will adjust other wedge's targets\n",
    "                        sumWedgePopGapChange += wedgePopGap[nW] - oldWedgePopGap\n",
    "                        if (nActiveWedges == 0) : #we're doomed, somehow all wedges are short and off map\n",
    "                            print(\"PUNT! no more active wedges for tract, loop =\",t,tractLoopCounter[t] )\n",
    "                            tractLoopCounter[t] = nGiveUp + 1  #PUNT !!!\n",
    "                            \n",
    "                        max_dr = [globalMax_dr]*nWedges   #allow other wedges to take big steps again to catch up\n",
    "                        yoyoCount = [0] * nWedges  #go back to original gains on ALL active wedges\n",
    "                   \n",
    "        # end of nW loop to adjust all wedge populations by growing or trimming wedges, IDing over-edge wedges\n",
    "        tractLoopCounter[t] +=1   # still looping on home district Pop.\n",
    "        nReceivingWedges = nActiveWedges\n",
    "        oppFlag = 0\n",
    "        if 0 == 1: #always false; for HD2 we would have checked here for opp wedge restriction; ignore this block\n",
    "            # targetWedgePop[int(nWedges/2)] < 0.99 * avgDistrictPop/float(nWedges): #is opp wedge restricted?\n",
    "            oppFlag = 1\n",
    "            nReceivingWedges -= 1-isOverEdge[int(nWedges/2)]  #if yes, don't count opposite wedge as a receiver\n",
    "        if nReceivingWedges == 0: #rarely, the opposite wedge is the only active one\n",
    "            for nWW in range(nWedges): #in this case, we allow the opposite wedge to pick up the slack\n",
    "                targetWedgePop[nWW] += sumWedgePopGapChange/max(1.,float(nReceivingWedges) )\n",
    "        else: #if the opp wedge has a ceiling wedgePopTarget, distribute wedgePopGap to other active wedges\n",
    "            for nWW in range(nWedges):  #time to make adjustments in target wedge pops based on boundary fails\n",
    "                if nWW != int(nWedges/2) or oppFlag == 0 :  #excluding flagged opposite wedge as a receiver\n",
    "                    targetWedgePop[nWW] += sumWedgePopGapChange/float(nReceivingWedges)\n",
    "\n",
    "        # *** NEW 1/19/22 CODE TO ADJUST WEDGE ANGLES WHEN A BOUNDARY ENCOUNTERED\n",
    "        if (nActiveWedges == nWedges or nActiveWedges < nWedges - 2\n",
    "           or didWeRestart[t] == 1) :  #0 or 3+ over-boundary short wedges, or we've already adjusted wedge angles once\n",
    "            HDpop = np.sum(wedgePop) #Keep going with normal wedge growth and trim process\n",
    "        else :  # 1 OR 2 SHORTED WEDGES.  MAY WANT TO ALTER WEDGE ANGLES\n",
    "            didWeRestart[t] = 1  #to ensure we adjust wedge angles and target wedgePops at most once per tract\n",
    "            if (nActiveWedges == nWedges - 2) :  #exactly two wedges got shorted in this loop -- are they opposing?\n",
    "                oppW = int( (shortedWedge + nWedges/2) % nWedges)  #the index of wedge opposite the last shorted wedge\n",
    "                if (isOverEdge[oppW] == 0) :\n",
    "                    print(\"we have 2 non-opposing shorted wedges for tract no\",t)\n",
    "                    totalLiveAngle = 2.*pi - np.sum(isOverEdge)*avgWedgeAngle  #avail angle to divvy among live wedges  \n",
    "                    for nW in range (nWedges) : #Decrease wedge angles opposite shorted wedges via a complex weighting\n",
    "                        if isOverEdge[nW] == 0 :\n",
    "                            oppW = int( (nW + nWedges/2) % nWedges) \n",
    "                            angleWeight = (np.sum(wedgePopGap)-wedgePopGap[oppW])/np.sum(wedgePopGap)/(nActiveWedges-1)\n",
    "                            wedgeAngle[nW] = angleWeight * totalLiveAngle  \n",
    "                else : #shorted wedges ARE opposing\n",
    "                    print(\"we have 2 OPPOSING shorted wedges for tract no\",t) # no wedge-angle adjustment in this case\n",
    "                    #wedgeAngle=[avgWedgeAngle]*nWedges  #comment this out, no change\n",
    "            else: # a single shorted wedge (over boundary).  COMPLETELY RESTART wedge growth process\n",
    "                shortW = shortedWedge #Next dozen lines: convoluted code to find angle to closest boundary point\n",
    "                #print(\"1 wedge over map boundary for tract\",t,\"at loop, wedgePop\",tractLoopCounter[t],wedgePop[shortW] )\n",
    "                shortedPoly = Polygon([tractCP, pt2[shortW],pt3[shortW] ])\n",
    "                MAPedge = shortedPoly.intersection(MAP.exterior) #true state boundary line where wedge crossed it\n",
    "                minDistance = max(tractCP.distance(MAPedge),tinyR)  #closest distance to edge where wedge hit the boundary\n",
    "                edgeCircle = tractCP.buffer(1.01*minDistance)  #build a circle a little bigger than this distance\n",
    "                #  1.001 is not big enough; will occasionally not intersect\n",
    "                closeArea = edgeCircle.intersection(MAPedge)\n",
    "                counter = 0.\n",
    "                maxCounter = 10. #give up after 10 tries\n",
    "                while closeArea.is_empty :  # protect against missing the map boundary somehow\n",
    "                    print(\"need to widen edge circle beyond\",minDistance, \"for tract (x,y)=(\",tractCP.x,tractCP.y)\n",
    "                    minDistance = minDistance * 1.1\n",
    "                    edgeCircle = tractCP.buffer(minDistance)  #widen the circle\n",
    "                    closeArea = edgeCircle.intersection(MAPedge)\n",
    "                    counter += 1\n",
    "                    if counter >= maxCounter: #not finding map edge intersection.  Try another way\n",
    "                        print(\"did not find boundary with circle approach.  Brute-force it.\")\n",
    "                        minDistance = tractCP.distance(MAP.exterior)\n",
    "                        edgeCircle = tractCP.buffer(1.01*minDistance)\n",
    "                        closeArea = edgeCircle.intersection(MAP.exterior)                        \n",
    "            \n",
    "                closePoint = closeArea.centroid  # this is a point very close to the closest beeline from tract to map edge\n",
    "                #print(closePoint,tractCP, \"are boundary point and tract center point\")\n",
    "                x1 = closePoint.x\n",
    "                x2 = tractCP.x  #debugging\n",
    "                dx = closePoint.x - tractCP.x\n",
    "                dy = closePoint.y - tractCP.y\n",
    "                exitAngle = pi/2. * np.sign(dy)  #default in case dx=0\n",
    "                if (dx != 0 ):\n",
    "                    exitAngle = math.atan(dy/dx)  #this is the angle from the x-axis to the exit beeline\n",
    "                    if dx < 0 :  #use complementary atan solution; boundary is west of tract centroid\n",
    "                        exitAngle = pi + exitAngle\n",
    "                angle0[t] = exitAngle # this reorients 0th wedge to face boundary's closest point               \n",
    "                # New 1/23/22 - let's estimate wedgePopGap at ideal orientation, normal angle   # ****************\n",
    "                wedgeAngle1 = exitAngle - 0.5*avgWedgeAngle\n",
    "                wedgeAngle2 = exitAngle + 0.5*avgWedgeAngle\n",
    "                maxR = max(stateWidth,stateHeight) #ensuring we make a big enough wedge\n",
    "                wedgePt1 = tractCP\n",
    "                wedgePt2 = Point(tractCP.x+maxR*math.cos(wedgeAngle1),  tractCP.y+maxR*math.sin(wedgeAngle1) )\n",
    "                wedgePt3 = Point(tractCP.x+maxR*math.cos(wedgeAngle2), tractCP.y+maxR*math.sin(wedgeAngle2) )\n",
    "                wedgePoly = Polygon([ wedgePt1, wedgePt2, wedgePt3 ])\n",
    "                minWedgePop[t] = 0. #the wedgePop of the shorted wedge if we re-orient but don't adjust angles\n",
    "                usedTract = [0]*nTracts  #rezero lists of tracts and precincts that could straddle multiple grids\n",
    "                for nG in range(nGrids) :  # loop over ACTIVE grids to look for intersecting tracts\n",
    "                    gridIntersxn = gridGeom[nG].intersection(wedgePoly)\n",
    "                    if (gridIntersxn.area > 0) :  #this grid is RELEVANT to this wedge\n",
    "                        for tt in range(nGridTracts[nG]) : #look for intersxns with all tracts in this grid\n",
    "                            nTT = gridTractNo[nG][tt] #shorthand for this tract's global tract no\n",
    "                            if(usedTract[nTT] == 0) :  #Did we not already look at this tract in another grid list?\n",
    "                                usedTract[nTT] = 1  #well, now we have!  Probe intersection with wedge\n",
    "                                overlap = tractGeom[nTT].intersection(wedgePoly).area\n",
    "                                if overlap > 0 :\n",
    "                                    fracArea = overlap/tractArea[nTT]\n",
    "                                    minWedgePop[t]  += fracArea*tractPop[nTT]  #always positive (used in density calc)\n",
    "                #print(\"the minimum shorted Wedge Pop for tract {0} is {1}\".format(t,minWedgePop[t]) )  #debug\n",
    "                #printAngle[t] = round(exitAngle*180./pi,4) #for debugging\n",
    "                printDist = round(minDistance,4)\n",
    "                #print(t,\"(\",tractCP.x,tractCP.y,\")\",printAngle,printDist,\"t,tract(x,y),exitAngle,dist\")\n",
    "                # ********************************************************************\n",
    "                # assign new wedge angles.  The two wide angles face toward and away from nearest boundary\n",
    "                #ratio = wedgePopGap[shortW]/ (avgDistrictPop/nWedges)  #0-1; this is degree of shortness\n",
    "                ratio = 1. - minWedgePop[t]/ (avgDistrictPop/nWedges)  # *** UPDATED\n",
    "                # printWedgePopGap[t] = wedgePopGap[shortW]\n",
    "                wideAngle = (1. + coeff1 * ratio + coeff2 * ratio*ratio)*avgWedgeAngle #HERE, WE ADJUST ANGLES\n",
    "                wideAngle = min(wideAngle, maxWideAngle*pi) #set an upper limit\n",
    "                thinAngle = (2.*pi - 2.*wideAngle)/(nWedges - 2.)  #other wedges equally angle-compressed\n",
    "                #if (t % 9 == 0):  #occasional print\n",
    "                    #print(\"tract,wide, thin angles are \",t,round(180/pi*wideAngle,4),round(180/pi*thinAngle,4) )\n",
    "                for nW in range(nWedges) :\n",
    "                    if (nW == 0 or nW == int(nWedges/2) ) : #assign these two as shorted wedge and its opposite\n",
    "                        wedgeAngle[nW] = wideAngle\n",
    "                    else :\n",
    "                        wedgeAngle[nW] = thinAngle\n",
    "                #** COMPLETELY RESTART WEDGE GROWTH PROCESS FROM INITIAL PIZZA SLICES, NOW RE-ORIENTED\n",
    "                loopTractUse = [0.] * nTracts  # reset as we are starting over\n",
    "                loopPrecinctUse = [0.]* nPrecincts\n",
    "                HDpop = 0.\n",
    "                nActiveWedges = nWedges\n",
    "                targetWedgePop = [avgDistrictPop/nWedges] * nWedges\n",
    "                oldR = [0.]*nWedges   #for remembering last loop's wedge radius  \n",
    "                currentR = [tinyR]*nWedges                \n",
    "                old_dr = [tinyR]*nWedges  #this will track the last radial step (to help convergence)\n",
    "                max_dr = [globalMax_dr]*nWedges  #reset max step size\n",
    "                wedgePop = [0.]*nWedges\n",
    "                oldWedgePop = [0.]*nWedges #wedge pop from previous round\n",
    "                wedgePopGap = [0.]*nWedges\n",
    "                isOverEdge = [0] *nWedges\n",
    "                isSatisfied = [0] *nWedges\n",
    "                yoyoCount = [0] *nWedges #just in case we missed this earlier\n",
    "                wedgeMaxR = [0.] * nWedges #resetting\n",
    "                angle[0] = angle0[t] - 0.5*wideAngle  #the 0th wedge always faces the boundary\n",
    "                angle2[0] = angle[0] + wedgeAngle[0]\n",
    "                for nW in range(nWedges) :\n",
    "                    if (nW == 0):\n",
    "                        angle[nW] = angle0[t] - 0.5*wideAngle  #we have to start somewhere :-)\n",
    "                    else :\n",
    "                        angle[nW] = angle2[nW-1]\n",
    "                    angle2[nW] = angle[nW]+wedgeAngle[nW]      \n",
    "                    pt1[nW] = tractCP\n",
    "                    pt2[nW] = Point(tractCP.x+tinyR*math.cos(angle[nW]),  tractCP.y+tinyR*math.sin(angle[nW]) )\n",
    "                    pt3[nW] = Point(tractCP.x+tinyR*math.cos(angle2[nW]), tractCP.y+tinyR*math.sin(angle2[nW]) )\n",
    "                    wedgePoly = Polygon([pt1[nW], pt2[nW], pt3[nW] ])\n",
    "                    wedgePop[nW] = tractPop[t]* wedgePoly.area/max(tractArea[t],minTractArea)\n",
    "                HDpop = np.sum(wedgePop)   #** END OF RESTART BLOCK.  RE-ENTER MAIN LOOP FOR THIS TRACT\n",
    "                      \n",
    "        # **** BELOW IS TO CORRECT FOR NONCONVERGENCE *****\n",
    "        if (tractLoopCounter[t] > nLoopPrint):  #may be becoming unstable. Alert user,    #and (don't) reduce gain\n",
    "            strPop = str(round(HDpop,2))\n",
    "            strWdg = \"Overedge?\"\n",
    "            strSat = \"Satisfied?\"\n",
    "            strYoyo = \"yoyo?\"                \n",
    "            strTWP_dr = \"tWP,dr\"\n",
    "            for nWW in range(nWedges) :\n",
    "                strPop = strPop + \", \"+str(round(wedgePop[nWW],1) )\n",
    "                strWdg = strWdg +str(isOverEdge[nWW])+\", \"\n",
    "                strSat = strSat +str(isSatisfied[nWW])\n",
    "                strYoyo = strYoyo + str(yoyoCount[nWW])\n",
    "                strTWP_dr = strTWP_dr + \", \"+str(round(targetWedgePop[nWW],1) )+\",\"+str(round(old_dr[nWW],4) )\n",
    "            print(\"loop{0}, tr{1},wedgePops{2}, {3},{4},{5} \".format(tractLoopCounter[t],t,strPop,strWdg,strSat,strYoyo) )\n",
    "            print(\"   targetWP, latest drx4 are\",strTWP_dr)\n",
    "\n",
    "        if (tractLoopCounter[t] > nLoopSuperPrint) :\n",
    "            for nWW in range(nWedges):                \n",
    "                print(\"wedge no, currentR,oldWedgePop, wedgePop, overEdge?\",nWW,str(round(currentR[nWW],5)), \n",
    "                      str(round(oldWedgePop[nWW],4)), str(round(wedgePop[nWW],4)),isOverEdge[nWW])   #debug\n",
    "                x, y = printPoly[nWW].exterior.xy     #wedge debugging .....\n",
    "                plt.plot(x, y, c=(0.1, 0.2, 0.02+float(nWW)/nWedges) )\n",
    "            plt.show()\n",
    "        if(tractLoopCounter[t] >= nGiveUp):\n",
    "            print(\"I looped {0} times on tract {1}, giving up w pop {2}\".format(nGiveUp,t,HDpop) )\n",
    "            wrongPop[t] = HDpop  #this will flag this HD as triaged\n",
    "            HDpop = avgDistrictPop  #white lie to kick out of loop\n",
    "        # *** END OF TRIAGE FOR NONCONVERGENCE\n",
    "        \n",
    "    # END OF WHILE LOOP --> we are within tolerance of avgDistrictPop. Finalize this tract's Home District stats\n",
    "    for nTT in range (nTracts) :\n",
    "        tractUse[nTT] += loopTractUse[nTT]\n",
    "    totGOP = 0.\n",
    "    totVote = 0.\n",
    "    totVAP = 0.\n",
    "    totHisp = 0.\n",
    "    totBlack = 0.  #zero these out prior to summing over final wedges\n",
    "    usedTract = [0]*nTracts  #rezero lists of tracts and precincts that could straddle multiple grids\n",
    "    usedPrecinct = [0]*nPrecincts\n",
    "\n",
    "    if (tractPop[t] < minTractPop or tractArea[t] == 0 or isSkippedTract[t] == 1): \n",
    "        #we bypassed the big loop; this tract is inconsequential\n",
    "        HDvPop[t] = HDpop  #a white lie\n",
    "        HDweight[t] = 0.000001  #to suppress this in total stats\n",
    "    else :\n",
    "        HDvPop[t] = np.sum(wedgePop)\n",
    "        # HDvHisp[t] = np.sum(wedgeHisp)/np.sum(wedgePop)  #3/2/22 - move to final wedge calcs\n",
    "        # HDvBlack[t] = np.sum(wedgeBlack)/np.sum(wedgePop)  #3/2/22 - move to final wedge calcs\n",
    "        HDweight[t] = tractPop[t]/np.sum(tractPop)\n",
    "        nearEdge[t] = nWedges - nActiveWedges  #flagging the number of wedges that were not completely pop-filled\n",
    "        centerPt = tractCP\n",
    "        outerPt2 = Point(tractCP.x+currentR[0]*math.cos(angle[0]), tractCP.y+currentR[0]*math.sin(angle[0]) )\n",
    "        outerPt3 = Point(tractCP.x+currentR[0]*math.cos(angle2[0]),tractCP.y+currentR[0]*math.sin(angle2[0]) )\n",
    "        HDpolly = Polygon([centerPt,outerPt2,outerPt3])   #initiate a polygon that will be the home district\n",
    "        for nW in range(nWedges):\n",
    "            HDradius[t][nW] = currentR[nW]\n",
    "            HDangle[t][nW] = angle[nW]\n",
    "            \n",
    "        for nW in range(1,nWedges):  #save final geometry, compute minority and partisan stats\n",
    "            cR = currentR[nW] #shorthand\n",
    "            outerPt2 = Point(tractCP.x+cR*math.cos(angle[nW]), tractCP.y+cR*math.sin(angle[nW]) )\n",
    "            outerPt3 = Point(tractCP.x+cR*math.cos(angle2[nW]),tractCP.y+cR*math.sin(angle2[nW]) )\n",
    "            HDpolly =  HDpolly.union( Polygon([centerPt,outerPt2,outerPt3]) )  #add this wedge to HD district polygon\n",
    "        HDpolly = HDpolly.intersection(MAP)  #exclude map's convex hull and buffer, just the original union of precincts\n",
    "        HDarea[t] = HDpolly.area  #for stats, final Home District area\n",
    "        for nG in range(nGrids) :  # ID the ACTIVE grids to look for intersecting precincts\n",
    "            gridIntersxn = gridGeom[nG].intersection(HDpolly)\n",
    "            if (gridIntersxn.area > 0) :  #this grid is RELEVANT to the final Home District\n",
    "                for tt in range(nGridTracts[nG]):\n",
    "                    nTT = gridTractNo[nG][tt]\n",
    "                    if usedTract[nTT] == 0: #only examine tracts that have not already been called for intersection\n",
    "                        usedTract[nTT] == 1  #this tract has now been called \n",
    "                        overlap = tractGeom[nTT].intersection(HDpolly).area\n",
    "                        if overlap > 0 :\n",
    "                            fracArea = overlap/tractArea[nTT]\n",
    "                            totVAP += fracArea*tractVAP[nTT]\n",
    "                            totHisp += fracArea*tractHisp[nTT]\n",
    "                            totBlack += fracArea*tractBlack[nTT]\n",
    "                        \n",
    "                for pp in range(nGridPrecincts[nG]): #scanning the list of precincts in this active grid\n",
    "                    nPP = gridPrecinctNo[nG][pp]\n",
    "                    if usedPrecinct[nPP] == 0  :\n",
    "                        usedPrecinct[nPP] = 1  #don't double up on precinct intersection\n",
    "                        overlap = vtdGeom[nPP].intersection(HDpolly).area\n",
    "                        if overlap > 0 :\n",
    "                            fracArea = overlap/vtdArea[nPP]\n",
    "                            totGOP += fracArea*vtdGOP[nPP]*vtdPop[nPP]\n",
    "                            totVote += fracArea*vtdPop[nPP]\n",
    "                            loopPrecinctUse[nPP] += overlap/vtdArea[nPP] *tractPop[t]/avgDistrictPop\n",
    "        HDvHisp[t] = totHisp/totVAP\n",
    "        HDvBlack[t] = totBlack/totVAP\n",
    "        HDvGOP[t] = totGOP / totVote\n",
    "        for nPP in range (nPrecincts) :\n",
    "            precinctUse[nPP] += loopPrecinctUse[nPP] #add to global use for this precinct\n",
    "            \n",
    "        \n",
    "\n",
    "    # end of loop on this tract\n",
    "for t in range(nTracts):\n",
    "    if(wrongPop[t] > 0):\n",
    "        HDvPop[t] = wrongPop[t]  #undo the lie that got us out of the loop early\n",
    "HDsumWeight = np.sum(HDweight)\n",
    "print(\"Sum of weights over all precinct home districts should have been 1.000, but was \",HDsumWeight)\n",
    "print(\"Average and max number of wedgePop loops per tract were: \",np.average(tractLoopCounter),np.max(tractLoopCounter) )\n",
    "print(\"min,average,max of Home District areas were: \",np.min(HDarea),np.average(HDarea),np.max(HDarea) )\n",
    "stateGOP2 = 0.\n",
    "stateHisp2 = 0.\n",
    "stateBlack2 = 0.\n",
    "for t in range(nTracts):\n",
    "    HDweight[t] = HDweight[t]/HDsumWeight   #renormalizing\n",
    "    stateGOP2 += HDweight[t]*HDvGOP[t]\n",
    "    stateBlack2 += HDweight[t]*HDvBlack[t]\n",
    "    stateHisp2 += HDweight[t]*HDvHisp[t]\n",
    "statePop = np.sum(tractPop)\n",
    "print(\"calculated statewide vote was {0}, should have been {1}\".format(stateGOP2, stateGOP) )\n",
    "print(\"calcd statewide Hispanic pop was {0}, should have been {1}\".format(stateHisp2, np.sum(tractHisp)/stateVAP ) )\n",
    "print(\"calcd statewide Black pop was {0}, should have been {1}\".format(stateBlack2, np.sum(tractBlack)/stateVAP ) )\n",
    "print(\"fraction of HDs that with altered wedge angles near boundaries = \",np.sum(didWeRestart)/nTracts)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "id": "24a347a9-8220-4c1a-bcfc-14923f1f98f7",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Sum of weights over all precinct home districs should have been 1.000, but was  1.000070852627229\n",
      "Average and max number of wedgePop loops per tract were:  7.181603773584905 35.0\n",
      "min,average,max of Home District areas were:  0.0 1.100514010510079 2.2882667835172055 for  IN\n"
     ]
    }
   ],
   "source": [
    "#use this line to document progress\n",
    "# start IN 8:29am, end 9:19\n",
    "print(\"Sum of weights over all precinct home districs should have been 1.000, but was \",HDsumWeight)\n",
    "print(\"Average and max number of wedgePop loops per tract were: \",np.average(tractLoopCounter),np.max(tractLoopCounter) )\n",
    "print(\"min,average,max of Home District areas were: \",np.min(HDarea),np.average(HDarea),np.max(HDarea),\"for \",STATE )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "id": "72247942-899a-4ab2-9796-239aa1e524fb",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "IN 18Mar\n"
     ]
    }
   ],
   "source": [
    "date = \"18Mar\"\n",
    "print(STATE, date)  #check that I will not overwrite existing file :-)\n",
    "tractCPx = [0.]*nTracts\n",
    "tractCPy = [0.]*nTracts\n",
    "tractNo = [0.]*nTracts\n",
    "for t in range(nTracts):\n",
    "    tractCPx[t]=tractGeom[t].centroid.x\n",
    "    tractCPy[t]=tractGeom[t].centroid.y\n",
    "    tractNo[t]=t"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "id": "c55ac5fd-7402-47b9-9093-10b9fe0912b6",
   "metadata": {},
   "outputs": [],
   "source": [
    "#LET'S WRITE AN OUTPUT FILE BEFORE WE FORGET :-)\n",
    "#convert HD wedge geometries to 1D arrays.  BELOW IS FOR 4-WEDGE.  CAN AUGMENT FOR 6-WEDGE\n",
    "HDangle0 = [0.]*nTracts\n",
    "HDradius0 = [0.]*nTracts\n",
    "HDangle1 = [0.]*nTracts\n",
    "HDradius1 = [0.]*nTracts\n",
    "HDangle2 = [0.]*nTracts\n",
    "HDradius2 = [0.]*nTracts\n",
    "HDangle3 = [0.]*nTracts\n",
    "HDradius3 = [0.]*nTracts\n",
    "for t in range(nTracts):\n",
    "    HDangle0[t] = HDangle[t][0]\n",
    "    HDradius0[t] = HDradius[t][0]\n",
    "    HDangle1[t] = HDangle[t][1]\n",
    "    HDradius1[t] = HDradius[t][1]\n",
    "    HDangle2[t] = HDangle[t][2]\n",
    "    HDradius2[t] = HDradius[t][2]\n",
    "    HDangle3[t] = HDangle[t][3]\n",
    "    HDradius3[t] = HDradius[t][3]\n",
    "# now convert output to pandas dataframe and export\n",
    "\n",
    "paramList = [\"STATE\",\"stateGOP\",\"nDistricts\",\"nTracts\",\"nPrecincts\",\"nWedges\",\"popn-toler\",\n",
    "             \"gain\",\"coeff1\",\"coeff2\"]\n",
    "paramValues = [STATE,stateGOP, nDistricts, nTracts,nPrecincts,nWedges, toler, gain, coeff1,coeff2]\n",
    "for i in range(nTracts-len(paramList)):\n",
    "    paramList.append(\".\")\n",
    "    paramValues.append(-99)  #so all columns have same number of entries, even the parameter list\n",
    "df = pd.DataFrame( {\"paramList\": paramList,\"paramValues\":paramValues,\"HD-pop\":HDvPop,\"HDvGOP\":HDvGOP,\"HDvHisp\":HDvHisp,\n",
    "                    \"HDvBlack\":HDvBlack,\"HDwt\":HDweight,\"HDarea\":HDarea, \"HDangle0\":HDangle0, \"HDangle1\":HDangle1,\n",
    "                    \"HDangle2\":HDangle2,\"HDangle3\":HDangle3,\"HDradius0\":HDradius0,\"HDradius1\":HDradius1,\n",
    "                    \"HDradius2\":HDradius2,\"HDradius3\":HDradius3,\"startAngle\":angle0,\"tractNo\":tractNo,\n",
    "                    \"Loops\":tractLoopCounter,\"tractPop\":tractPop,\"tractHisp\":tractHisp,\"tractBlack\":tractBlack,\n",
    "                    \"centroid x\":tractCPx,\"centroid y\":tractCPy, \"tractUse\":tractUse,\"nearEdge\":nearEdge,\n",
    "                    \"wrongPop\":wrongPop,\"Restart?\":didWeRestart} ) \n",
    "\n",
    "outname = STATE+str(nDistricts)+\"HD1tol\"+str(toler)+\"nW\"+str(nWedges)+date+\".csv\"\n",
    "outpath = \"state_HD_output/\"+outname\n",
    "df.to_csv(outpath)\n",
    "precinctNo = [0.]*nPrecincts\n",
    "vtdX = [0.]*nPrecincts\n",
    "vtdY = [0.]*nPrecincts\n",
    "for p in range(nPrecincts):\n",
    "    precinctNo[p]=p\n",
    "    vtdX[p] = vtdGeom[p].centroid.x\n",
    "    vtdY[p] = vtdGeom[p].centroid.y\n",
    "df2 = pd.DataFrame( {\"precinctNo\":precinctNo,\"precinctPop\":vtdPop,\"precUse\":precinctUse, \"vtdX\":vtdX, \"vtdY\":vtdY} )\n",
    "outname2 = STATE+date+\"_VTD_tol\"+str(toler)+\"nW\"+str(nWedges)+\".csv\"\n",
    "outpath = \"state_HD_output/\"+outname2\n",
    "df2.to_csv(outpath)  #currently, I'm outputting the precinct use stats to a separate file."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "id": "c4449da0-47a9-40cc-a585-8288447290e6",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "we will redo tract numbers  [0]\n"
     ]
    }
   ],
   "source": [
    "# Looking for nonconvergers ...  Run even if none observed, just to check  \n",
    "redo = [0]\n",
    "counter = 0.\n",
    "for t in range (nTracts) :\n",
    "    if wrongPop[t] > 0 :\n",
    "        ratio = round(wrongPop[t]/avgDistrictPop,2)\n",
    "        tractX = tractGeom[t].centroid.x\n",
    "        tractY = tractGeom[t].centroid.y        \n",
    "        print(t, ratio,\"(\",tractX,tractY,\")\",HDvGOP[t], \"t, pop/target,(x,y), pctR\")\n",
    "        if (ratio < 0.9 or ratio > 1.1) :  #these we will redo\n",
    "            plt.text(tractGeom[t].centroid.x, tractGeom[t].centroid.y,ratio, fontsize=14)\n",
    "            if counter == 0 :  #first redo\n",
    "                redo = [t]\n",
    "            else :\n",
    "                redo.append(t)\n",
    "            counter+= 1\n",
    "        else: #show these milder offenders on the map in a smaller font            \n",
    "            plt.text(tractGeom[t].centroid.x, tractGeom[t].centroid.y,ratio, fontsize=9)\n",
    "\n",
    "x,y = tractMAP.exterior.xy\n",
    "plt.plot(x,y,c=\"purple\")\n",
    "plt.show()\n",
    "print(\"we will redo tract numbers \",redo)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "id": "be6e965b-4ca8-4af2-8031-63132fbb939d",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "139 0.7757407720991434 321 pop,GOP 0.7570093457943925\n",
      "193 0.7674387763847431 326 pop,GOP 0.7975460122699386\n",
      "218 0.7548072294876539 406 pop,GOP 0.7955665024630542\n",
      "221 0.7998634236971851 573 pop,GOP 0.8045375218150087\n",
      "224 0.7200029907564138 397 pop,GOP 0.7128463476070529\n",
      "467 0.7328045994513546 635 pop,GOP 0.710236220472441\n",
      "479 0.7978607798852185 2578 pop,GOP 0.6314972847168348\n",
      "487 0.7233808357236319 3088 pop,GOP 0.5744818652849741\n",
      "488 0.7723924952065916 2427 pop,GOP 0.6168108776266996\n",
      "489 0.7482648293933956 825 pop,GOP 0.64\n",
      "490 0.7503303492261253 674 pop,GOP 0.6246290801186943\n",
      "492 0.6480344185262129 2543 pop,GOP 0.6032245379473063\n",
      "493 0.7589979615911624 778 pop,GOP 0.6568123393316195\n",
      "496 0.7579666561049017 519 pop,GOP 0.6589595375722543\n",
      "497 0.6812627142535934 2602 pop,GOP 0.59684857801691\n",
      "498 0.7082933226350279 2353 pop,GOP 0.6090097747556311\n",
      "499 0.6783741578495052 2248 pop,GOP 0.5791814946619217\n",
      "500 0.7859373779589408 351 pop,GOP 0.7264957264957265\n",
      "501 0.76288174854445 860 pop,GOP 0.6255813953488372\n",
      "502 0.7394722086628829 1095 pop,GOP 0.5945205479452055\n",
      "503 0.7088347844702214 730 pop,GOP 0.5835616438356165\n",
      "504 0.7026656190042693 431 pop,GOP 0.531322505800464\n",
      "505 0.6789627678574727 576 pop,GOP 0.6336805555555556\n",
      "506 0.7700317549064395 1018 pop,GOP 0.6581532416502947\n",
      "507 0.7120183028749147 873 pop,GOP 0.6231386025200458\n",
      "508 0.7810851692740037 679 pop,GOP 0.5994108983799705\n",
      "509 0.6745053125615482 2775 pop,GOP 0.5953153153153153\n",
      "510 0.7478502046304595 329 pop,GOP 0.6291793313069909\n",
      "511 0.7218917136549592 2942 pop,GOP 0.592114208021754\n",
      "512 0.6824471355903156 256 pop,GOP 0.57421875\n",
      "513 0.6797100296641672 2836 pop,GOP 0.610719322990127\n",
      "514 0.7220556457730547 633 pop,GOP 0.5813586097946287\n",
      "515 0.6815019779462733 2391 pop,GOP 0.5955667084901715\n",
      "516 0.7371674291971375 336 pop,GOP 0.5535714285714286\n",
      "517 0.7970248495683698 323 pop,GOP 0.6687306501547987\n",
      "518 0.7317892053551025 564 pop,GOP 0.5921985815602837\n",
      "519 0.7057548418027311 2555 pop,GOP 0.5996086105675147\n",
      "520 0.7106084156391699 3095 pop,GOP 0.6135702746365105\n",
      "521 0.6722046531822895 2586 pop,GOP 0.6117556071152359\n",
      "522 0.7802962181127857 693 pop,GOP 0.6767676767676768\n",
      "726 0.7846332400709476 416 pop,GOP 0.5625\n",
      "727 0.7888085299133761 450 pop,GOP 0.5777777777777777\n",
      "729 0.7849766525862719 591 pop,GOP 0.2622673434856176\n",
      "730 0.7510036910023815 352 pop,GOP 0.48863636363636365\n",
      "731 0.7703488621557366 526 pop,GOP 0.4296577946768061\n",
      "733 0.7474385092017527 650 pop,GOP 0.42\n",
      "734 0.7808318153477785 483 pop,GOP 0.453416149068323\n",
      "742 0.7523154932633109 355 pop,GOP 0.39154929577464787\n",
      "743 0.7537325729487567 639 pop,GOP 0.38341158059467917\n",
      "747 0.7865886842289318 517 pop,GOP 0.47775628626692457\n",
      "748 0.7445269258047876 700 pop,GOP 0.46714285714285714\n",
      "750 0.792592741187432 1564 pop,GOP 0.4514066496163683\n",
      "751 0.7371989539448388 635 pop,GOP 0.5448818897637795\n",
      "756 0.7754214753788322 694 pop,GOP 0.49135446685878964\n",
      "759 0.7795574183434292 541 pop,GOP 0.43253234750462105\n",
      "760 0.7394995294869515 503 pop,GOP 0.4850894632206759\n",
      "761 0.7457313525328993 301 pop,GOP 0.5415282392026578\n",
      "762 0.7461786913129193 790 pop,GOP 0.44556962025316454\n",
      "774 0.7664049456258573 926 pop,GOP 0.468682505399568\n",
      "782 0.7936795712936596 316 pop,GOP 0.6360759493670886\n",
      "784 0.7826378645171297 26 pop,GOP 0.5\n",
      "884 1.4305099764514996e-20 2663 pop,GOP 0.6492677431468269\n",
      "912 1.140485575387736e-17 429 pop,GOP 0.8484848484848485\n",
      "957 0.761003534322452 537 pop,GOP 0.7318435754189944\n",
      "958 0.7273556935085717 632 pop,GOP 0.7341772151898734\n",
      "959 0.7738688350562624 351 pop,GOP 0.6296296296296297\n",
      "960 0.7730423541473344 302 pop,GOP 0.6158940397350994\n",
      "961 0.7819916881335772 242 pop,GOP 0.6652892561983471\n",
      "967 0.7453885440240059 402 pop,GOP 0.736318407960199\n",
      "968 0.7254123316408965 463 pop,GOP 0.7300215982721382\n",
      "985 0.797961003653464 336 pop,GOP 0.5654761904761905\n",
      "1099 0.7047535775916789 1671 pop,GOP 0.6157989228007181\n",
      "1122 0.6692697642667306 2363 pop,GOP 0.5641134151502327\n",
      "1135 0.620244220150842 1510 pop,GOP 0.5496688741721855\n",
      "1139 0.6526511675049786 2614 pop,GOP 0.5849273144605968\n",
      "1143 0.6324193491919732 2521 pop,GOP 0.5874652915509718\n",
      "1146 0.6333709044245901 2506 pop,GOP 0.5841979249800479\n",
      "1147 0.6435855011704387 2876 pop,GOP 0.5900556328233658\n",
      "1152 0.7226569782693982 2015 pop,GOP 0.5985111662531017\n",
      "1154 0.6600837540625547 2665 pop,GOP 0.5842401500938086\n",
      "1157 0.6760357448814874 2366 pop,GOP 0.5684699915469146\n",
      "1170 0.6933685699957344 12 pop,GOP 0.75\n",
      "1184 0.632251135650395 2592 pop,GOP 0.5821759259259259\n",
      "1188 0.6436460453616405 2534 pop,GOP 0.5970797158642462\n",
      "1199 0.627187878618115 441 pop,GOP 0.3968253968253968\n",
      "1223 0.6257759285153334 563 pop,GOP 0.5346358792184724\n",
      "1337 0.7956447509663702 446 pop,GOP 0.6121076233183856\n",
      "1338 0.7716797373929695 756 pop,GOP 0.6878306878306878\n",
      "1366 0.7998522978356694 84 pop,GOP 0.44047619047619047\n",
      "1367 0.7723625445344448 840 pop,GOP 0.6607142857142857\n",
      "1375 0.7889324895705256 329 pop,GOP 0.5258358662613982\n",
      "1438 0.7961438241880016 828 pop,GOP 0.8285024154589372\n",
      "1439 0.7691140702701261 536 pop,GOP 0.8339552238805971\n",
      "1440 0.7779712515394382 1239 pop,GOP 0.8224374495560937\n",
      "1441 0.7963029352164679 5 pop,GOP 0.6\n",
      "1485 0.7754287006239935 599 pop,GOP 0.5292153589315526\n",
      "1491 0.7556646986763341 4 pop,GOP 0.75\n",
      "1492 0.7752616964351807 592 pop,GOP 0.6097972972972973\n",
      "1493 0.7681890579888345 1019 pop,GOP 0.6035328753680078\n",
      "1497 0.7987438540057278 689 pop,GOP 0.5761973875181422\n",
      "1499 0.7892068134781624 817 pop,GOP 0.5667074663402693\n",
      "1505 0.7833469070910235 913 pop,GOP 0.6254107338444688\n",
      "1544 0.7958654665822058 457 pop,GOP 0.7943107221006565\n",
      "1545 0.7571659855082233 633 pop,GOP 0.7914691943127962\n",
      "1547 0.7855111639295116 1 pop,GOP 1.0\n",
      "1558 0.7835763729775628 1123 pop,GOP 0.6215494211932324\n",
      "1563 0.7964860906775504 781 pop,GOP 0.5813060179257362\n",
      "1564 0.7777949646534286 601 pop,GOP 0.64891846921797\n",
      "1566 0.7737425693487953 1175 pop,GOP 0.6697872340425531\n",
      "1569 0.7807881301805032 714 pop,GOP 0.7044817927170869\n",
      "1574 0.762457708528746 471 pop,GOP 0.6645435244161358\n",
      "1577 0.7905145154445441 740 pop,GOP 0.7932432432432432\n",
      "2061 0.788327630322141 111 pop,GOP 0.7477477477477478\n",
      "2062 0.7827313660219711 200 pop,GOP 0.685\n",
      "2063 0.787685136240165 283 pop,GOP 0.6607773851590106\n",
      "2064 0.780071160604179 207 pop,GOP 0.6038647342995169\n",
      "2065 0.7882938565891994 306 pop,GOP 0.7483660130718954\n",
      "2067 0.7743148164204273 62 pop,GOP 0.7741935483870968\n",
      "2255 0.0011773623769312598 711 pop,GOP 0.8115330520393812\n",
      "2263 0.379314963586758 563 pop,GOP 0.6731793960923623\n",
      "2270 0.7023195532968939 2931 pop,GOP 0.6383486864551348\n",
      "2272 0.571064143518919 487 pop,GOP 0.5831622176591376\n",
      "2280 0.0006139450328638717 2363 pop,GOP 0.6144731273804486\n",
      "2281 0.0014429725625901403 2257 pop,GOP 0.6136464333185645\n",
      "2860 0.7893338222879337 726 pop,GOP 0.5055096418732782\n",
      "3732 0.7751274437972322 428 pop,GOP 0.780373831775701\n",
      "3735 0.735767141631177 256 pop,GOP 0.76953125\n",
      "3736 0.7651118508570893 367 pop,GOP 0.8283378746594006\n",
      "3798 0.6874842479555565 312 pop,GOP 0.07051282051282051\n",
      "3805 0.6492081919380293 530 pop,GOP 0.330188679245283\n",
      "3811 0.7409738377725745 356 pop,GOP 0.025280898876404494\n",
      "3821 0.7340997195431762 523 pop,GOP 0.11089866156787763\n",
      "3825 0.784879262492472 809 pop,GOP 0.5760197775030902\n",
      "3828 0.7815576901974337 739 pop,GOP 0.08389715832205684\n",
      "3829 0.7697941845856952 1145 pop,GOP 0.662882096069869\n",
      "3839 0.6756158844473956 938 pop,GOP 0.4541577825159915\n",
      "3842 0.7139054643651506 502 pop,GOP 0.035856573705179286\n",
      "3844 0.768013045349253 314 pop,GOP 0.42356687898089174\n",
      "3846 0.7397430605817976 438 pop,GOP 0.6506849315068494\n",
      "3852 0.7437540077584553 785 pop,GOP 0.6509554140127388\n",
      "3853 0.618819061318174 544 pop,GOP 0.42463235294117646\n",
      "3854 0.6356486435254196 831 pop,GOP 0.3622141997593261\n",
      "3855 0.7725036663047348 482 pop,GOP 0.495850622406639\n",
      "3856 0.694593286734125 613 pop,GOP 0.5628058727569332\n",
      "3861 0.6697918286780639 446 pop,GOP 0.4461883408071749\n",
      "3863 0.7301240412749869 962 pop,GOP 0.42723492723492723\n",
      "3870 0.7215432200862479 740 pop,GOP 0.6040540540540541\n",
      "3871 0.7037191823248267 492 pop,GOP 0.43902439024390244\n",
      "3873 0.6783865458607512 583 pop,GOP 0.12178387650085763\n",
      "3878 0.7261617719997583 915 pop,GOP 0.5038251366120219\n",
      "3885 0.739557622701607 591 pop,GOP 0.4500846023688663\n",
      "3889 0.6198743511145196 608 pop,GOP 0.3355263157894737\n",
      "3890 0.668290530615477 477 pop,GOP 0.38784067085953877\n",
      "3892 0.779157925718499 377 pop,GOP 0.058355437665782495\n",
      "3894 0.7232185741395297 572 pop,GOP 0.3304195804195804\n",
      "3895 0.6860663556745622 761 pop,GOP 0.44021024967148487\n",
      "3899 0.6373220154689606 720 pop,GOP 0.4027777777777778\n",
      "3903 0.6798739621045631 441 pop,GOP 0.1360544217687075\n",
      "3909 0.6811758726575633 310 pop,GOP 0.1935483870967742\n",
      "3910 0.7851376716291152 482 pop,GOP 0.23443983402489627\n",
      "3911 0.7651571929656368 501 pop,GOP 0.3273453093812375\n",
      "3912 0.6166725590945095 521 pop,GOP 0.15930902111324377\n",
      "3914 0.6805005833411493 448 pop,GOP 0.546875\n",
      "3915 0.7264026499813453 528 pop,GOP 0.42045454545454547\n",
      "3916 0.7778635985716043 633 pop,GOP 0.5339652448657188\n",
      "3917 0.75851558238781 393 pop,GOP 0.08651399491094147\n",
      "3919 0.7787850506671701 840 pop,GOP 0.4785714285714286\n",
      "3921 0.7788818860284376 675 pop,GOP 0.4888888888888889\n",
      "3923 0.7846601320348965 501 pop,GOP 0.10978043912175649\n",
      "3924 0.7710960538821184 376 pop,GOP 0.3191489361702128\n",
      "3925 0.7690567219638549 534 pop,GOP 0.5355805243445693\n",
      "3926 0.6212820971605026 724 pop,GOP 0.44613259668508287\n",
      "3927 0.7033198276578906 492 pop,GOP 0.2804878048780488\n",
      "3929 0.7093300478318249 624 pop,GOP 0.46474358974358976\n",
      "3932 0.6932950701486443 595 pop,GOP 0.4638655462184874\n",
      "3933 0.7076577276975848 546 pop,GOP 0.4084249084249084\n",
      "3935 0.735719216707161 559 pop,GOP 0.38640429338103754\n",
      "3936 0.6308942356614395 473 pop,GOP 0.3488372093023256\n",
      "3937 0.6442565348334828 354 pop,GOP 0.2994350282485876\n",
      "3939 0.7100220632805687 494 pop,GOP 0.4433198380566802\n",
      "3940 0.6742999165518146 425 pop,GOP 0.1952941176470588\n",
      "3942 0.7846382877547902 439 pop,GOP 0.08200455580865604\n",
      "3945 0.7758339287298289 427 pop,GOP 0.48243559718969553\n",
      "3946 0.7626289942027537 373 pop,GOP 0.16085790884718498\n",
      "3952 0.775133148238217 529 pop,GOP 0.08695652173913043\n",
      "3953 0.7295483919496758 546 pop,GOP 0.45054945054945056\n",
      "3954 0.7586857531351403 424 pop,GOP 0.3608490566037736\n",
      "3955 0.6673299047063362 639 pop,GOP 0.24569640062597808\n",
      "3956 0.6380976248456899 621 pop,GOP 0.37359098228663445\n",
      "3959 0.6454180179812421 378 pop,GOP 0.29365079365079366\n",
      "3960 0.7080243080709905 645 pop,GOP 0.44031007751937984\n",
      "3962 0.7271668031150471 444 pop,GOP 0.4752252252252252\n",
      "3963 0.7870211467437727 423 pop,GOP 0.5271867612293144\n",
      "3964 0.6177147938921528 450 pop,GOP 0.06888888888888889\n",
      "3965 0.7984462067450967 828 pop,GOP 0.5265700483091788\n",
      "3966 0.7340083488547473 802 pop,GOP 0.3491271820448878\n",
      "3967 0.7986501573209069 579 pop,GOP 0.6701208981001727\n",
      "3968 0.7614531822323292 701 pop,GOP 0.4550641940085592\n",
      "3969 0.6954154894967954 552 pop,GOP 0.30434782608695654\n",
      "3971 0.6822701179815064 808 pop,GOP 0.41955445544554454\n",
      "3972 0.6579663759356836 450 pop,GOP 0.03777777777777778\n",
      "3973 0.7061165409644112 836 pop,GOP 0.5681818181818182\n",
      "3976 0.7640577746125892 659 pop,GOP 0.44916540212443096\n",
      "3977 0.7180314231260175 760 pop,GOP 0.3894736842105263\n",
      "3980 0.7893018121560494 774 pop,GOP 0.39276485788113696\n",
      "3981 0.6521876554998733 565 pop,GOP 0.4743362831858407\n",
      "3984 0.6454927578621076 551 pop,GOP 0.38656987295825773\n",
      "3987 0.7401933123680835 635 pop,GOP 0.4566929133858268\n",
      "3999 0.6501852980872063 464 pop,GOP 0.21767241379310345\n",
      "4000 0.6515233966519532 469 pop,GOP 0.23880597014925373\n",
      "4001 0.7714246843774858 848 pop,GOP 0.4846698113207547\n",
      "4004 0.6955524956250105 702 pop,GOP 0.5612535612535613\n",
      "4005 0.645693133732425 488 pop,GOP 0.375\n",
      "4007 0.6859453773017188 861 pop,GOP 0.5470383275261324\n",
      "4011 0.6801819657188098 491 pop,GOP 0.2668024439918534\n",
      "4014 0.7525963854381242 1023 pop,GOP 0.4975562072336266\n",
      "4015 0.7422244317303877 993 pop,GOP 0.4743202416918429\n",
      "4017 0.6770944309422057 506 pop,GOP 0.17588932806324112\n",
      "4023 0.7356257717234205 714 pop,GOP 0.6246498599439776\n",
      "4027 0.585597240679057 564 pop,GOP 0.12234042553191489\n",
      "4031 0.6871253926123809 887 pop,GOP 0.322435174746336\n",
      "4035 0.6256684954108985 799 pop,GOP 0.37296620775969963\n",
      "4041 0.6529192759091972 506 pop,GOP 0.33794466403162055\n",
      "4045 0.7552923912866862 586 pop,GOP 0.5068259385665529\n",
      "4049 0.7102366733171437 941 pop,GOP 0.4814027630180659\n",
      "4050 0.7257082231809039 879 pop,GOP 0.49829351535836175\n",
      "4051 0.7425398017482605 726 pop,GOP 0.32231404958677684\n",
      "4054 0.5901249629413202 325 pop,GOP 0.31076923076923074\n",
      "4056 0.6099456783824513 534 pop,GOP 0.2247191011235955\n",
      "4059 0.7965060314986486 597 pop,GOP 0.6515912897822446\n",
      "4060 0.6441773112128754 505 pop,GOP 0.24356435643564356\n",
      "4063 0.7716656456310746 724 pop,GOP 0.49171270718232046\n",
      "4070 0.7432266502417096 985 pop,GOP 0.6548223350253807\n",
      "4095 0.7494855859027435 708 pop,GOP 0.5918079096045198\n",
      "4103 0.7613678968557221 526 pop,GOP 0.4372623574144487\n",
      "4111 0.6757722283625147 566 pop,GOP 0.4169611307420495\n",
      "4112 0.7848739103793613 562 pop,GOP 0.4092526690391459\n",
      "4116 0.7879073480723271 981 pop,GOP 0.7084607543323139\n",
      "4118 0.623704754349381 1112 pop,GOP 0.4685251798561151\n",
      "4119 0.6417473259676825 812 pop,GOP 0.5369458128078818\n",
      "4120 0.6524552882810548 842 pop,GOP 0.5095011876484561\n",
      "4121 0.6596879331325269 559 pop,GOP 0.5992844364937389\n",
      "4122 0.6296394859228196 643 pop,GOP 0.3483670295489891\n",
      "4123 0.5979460782017313 274 pop,GOP 0.23357664233576642\n",
      "4124 0.5747937094525433 514 pop,GOP 0.23929961089494164\n",
      "4125 0.5922400712272166 910 pop,GOP 0.321978021978022\n",
      "4142 0.5813386046047857 747 pop,GOP 0.23560910307898258\n",
      "4143 0.6062264071830106 665 pop,GOP 0.38345864661654133\n",
      "4147 0.6002966681514114 389 pop,GOP 0.4267352185089974\n",
      "4148 0.6024917925899902 691 pop,GOP 0.35311143270622286\n",
      "4149 0.6205293214863977 704 pop,GOP 0.34375\n",
      "4153 0.777607174793757 849 pop,GOP 0.6089517078916372\n",
      "4155 0.698028464978294 1033 pop,GOP 0.643756050338819\n",
      "4156 0.7176139848836517 653 pop,GOP 0.5604900459418071\n",
      "4165 0.6867820574335007 814 pop,GOP 0.6953316953316954\n",
      "4707 0.7998761546575685 678 pop,GOP 0.7846607669616519\n",
      "4771 0.3390165334093327 162 pop,GOP 0.35185185185185186\n"
     ]
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    {
     "data": {
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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Plotting underused PRECINCTS\n",
    "for p in range (nPrecincts) :\n",
    "    if precinctUse[p] < 0.8 and isSkippedPrecinct[p] == 0 and vtdPop[p] > 0: #ignore suppressed precincts\n",
    "        print(p,precinctUse[p], vtdPop[p],\"pop,GOP\",vtdGOP[p])\n",
    "        pU = round(precinctUse[p],3)\n",
    "        tractX = vtdGeom[p].centroid.x\n",
    "        tractY = vtdGeom[p].centroid.y        \n",
    "        # print(t, ratio,\"(\",tractX,tractY,\")\",HDvGOP[t], \"t, pop/target,(x,y), pctR\")\n",
    "        plt.text(vtdGeom[p].centroid.x, vtdGeom[p].centroid.y,pU, fontsize=9)\n",
    "        if notPolyVTD[p] == 0 :\n",
    "            x,y = vtdGeom[p].exterior.xy\n",
    "            plt.plot(x,y)\n",
    "        else :\n",
    "            for geom in vtdGeom[p].geoms:\n",
    "                x,y = geom.exterior.xy\n",
    "                plt.plot(x,y)\n",
    "\n",
    "x,y = wholeMAP.exterior.xy\n",
    "plt.plot(x,y,c=\"purple\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "id": "85429aa8-54b2-4e0b-b7e6-ef98c5a282bc",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "884 1.4305099764514996e-20 2663 pop,GOP 0.6492677431468269\n",
      "912 1.140485575387736e-17 429 pop,GOP 0.8484848484848485\n",
      "2255 0.0011773623769312598 711 pop,GOP 0.8115330520393812\n",
      "2280 0.0006139450328638717 2363 pop,GOP 0.6144731273804486\n",
      "2281 0.0014429725625901403 2257 pop,GOP 0.6136464333185645\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Plotting underused PRECINCTS\n",
    "for p in range (nPrecincts) :\n",
    "    if precinctUse[p] < 0.3 and isSkippedPrecinct[p] == 0 and vtdPop[p] > 0: #ignore suppressed precincts\n",
    "        print(p,precinctUse[p], vtdPop[p],\"pop,GOP\",vtdGOP[p])\n",
    "        pU = round(precinctUse[p],3)\n",
    "        tractX = vtdGeom[p].centroid.x\n",
    "        tractY = vtdGeom[p].centroid.y        \n",
    "        # print(t, ratio,\"(\",tractX,tractY,\")\",HDvGOP[t], \"t, pop/target,(x,y), pctR\")\n",
    "        plt.text(vtdGeom[p].centroid.x, vtdGeom[p].centroid.y,pU, fontsize=9)\n",
    "        if notPolyVTD[p] == 0 :\n",
    "            x,y = vtdGeom[p].exterior.xy\n",
    "            plt.plot(x,y)\n",
    "        else :\n",
    "            for geom in vtdGeom[p].geoms:\n",
    "                x,y = geom.exterior.xy\n",
    "                plt.plot(x,y)\n",
    "\n",
    "x,y = wholeMAP.exterior.xy\n",
    "plt.plot(x,y,c=\"purple\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 350,
   "id": "37f47a77-d58e-4b4c-be60-676819128e21",
   "metadata": {},
   "outputs": [],
   "source": [
    "# (OPTIONAL) IF WE STOPPED AND RESTARTED, read the data back in:  #NEED TO UPDATE THIS W/ANGLES, RADII\n",
    "import pandas as pd\n",
    "infilename = \"FL_nD28tol0.01nW4.csv\"\n",
    "df2 = pd.read_csv(\"state_HD_output/\"+infilename)\n",
    "HDvPop = df2[\"HD-pop\"]\n",
    "HDvHisp = df2[\"HDvHisp\"]\n",
    "HDvGOP = df2['HDvGOP']\n",
    "HDweight = df2['HDwt']\n",
    "HDarea = df2['HDarea']\n",
    "tractLoopCounter = df2['Loops']\n",
    "tractPop = df2['tractPop']\n",
    "tractCPx = df2['centroid x']\n",
    "tractCPy = df2['centroid y']\n",
    "tractUse = df2['tractUse']\n",
    "# nearEdge = df2['nearEdge']   #add this back in, PLEASE\n",
    "df2.head()\n",
    "nTracts = len(tractPop)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "id": "e9b2c0c0-fb53-44e5-9155-1963d4833ee8",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "750596.7418101031 0.1131774380110593 0.0001361943772 0.0400616118781349 0.7450137754374908\n"
     ]
    }
   ],
   "source": [
    "#  HERE'S A BLOCK TO OVERWRITE THE REDONE DATA\n",
    "for r in range(nRedos):\n",
    "    t = redo[r]\n",
    "    HDvPop[t] = redoHDvPop[r]\n",
    "    HDvGOP[t] = redoHDvGOP[r]\n",
    "    tractArea[t] = redoHDarea[r]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "id": "c80e0880-6811-4408-b847-6d70bd2546ff",
   "metadata": {},
   "outputs": [],
   "source": [
    "import matplotlib.pyplot as plt   #SKIP THIS BLOCK IF STARTING FROM FIRST BLOCK\n",
    "from scipy.stats import norm\n",
    "import numpy as np"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "id": "c8f73bb9-e36b-41c7-9220-1ec09169aa3e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# all done with redo's.  Let's look at some outputs.  First, red/blue by home district  SHOULD BE MIX OF RED AND BLUE\n",
    "nPlot = 5\n",
    "for t in range(nTracts):\n",
    "    if(t % nPlot == 0 and tractPop[t] > minTractPop):\n",
    "        redd = min(max( 0, ( HDvGOP[t] - 0.5) * 3.0 ),1)\n",
    "        bluu = min(max( 0, (0.5 - HDvGOP[t]) * 3.0 ),1)\n",
    "        plt.scatter(tractCPx[t],tractCPy[t],marker='.',color=(redd, 0,bluu ) )\n",
    "x,y = wholeMAP.exterior.xy\n",
    "plt.plot(x,y,color='green')\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "id": "a5d3b510-6d1e-49ae-9a5d-03352e2b449e",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "map of tracts that were used less than 0.7 of expectation in IN\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Now, where are those UNDERPERFORMING tracts (<0.x usage)\n",
    "maxPlot = 0.70\n",
    "print(\"map of tracts that were used less than {0} of expectation in {1}\".format(maxPlot, STATE) )\n",
    "for t in range(nTracts):\n",
    "    if(tractUse[t] < maxPlot and tractUse[t] > 0.05):  #ignore skipped tracts\n",
    "        redd = min(max( 0, ( HDvGOP[t] - 0.5) * 3.0 ),1)\n",
    "        bluu = min(max( 0, (0.5 - HDvGOP[t]) * 3.0 ),1)\n",
    "        plt.scatter(tractCPx[t],tractCPy[t],marker='.',color=(redd, 0,bluu ) )\n",
    "\n",
    "x,y = wholeMAP.exterior.xy   #turn these on if I pull map back in\n",
    "plt.plot(x,y,c=\"green\")\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "id": "876011e8-d37e-44a6-8dd9-a1e4d2f51e58",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "here is a map of tracts that were used more than 1.2 of expectation\n"
     ]
    },
    {
     "data": {
      "image/png": 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/TGV5OdLhoKKsjI+mTGFXBDx7JfQKhUIRZHYaBi9lZbHp22+RTnemi5TkrFjBK+efH3axV0If56jOWEU8kWsYLJk6ldwQC+02XafSbgenEzMuEfT8rLTb2abrIW2/OuEQehWjbwCozlhFrJNrGLydlYXDbsdssXCzzUZ6iOLlPa1WEiwWV1tmMyYpcVRUAJBgsdDTag1JuzWhhD7OUZ2xinghW9dx2O1IhwOH3U62rodM6LtrGg/YbGzTdXparQhg+dy5AAyfOJFuYe6Q9Qh9KPvilNA3AFRnrCLWybBaMXu8bIuFjCB41TsMg626Ti+rlcxq4t1d06pk2IRb3H3xOHTKo1coFDFNuqZxs81Gtq6TYbXW25vfYRj8wyd18kGb7SSxjxZU6CbOUZ2xingiXdOCFq7Z6u5w9YSCtup6XAu9yrppAKjOWIWidvRyd7iazGbMFgu9wtzBWhuURx/nqM5YhaJuZGoaD9psNcboowkl9HFOhdOV8pVoSoywJQpFwyNT06Ja4D1EVehGCGEWQqwRQsx3r7cUQnwjhNju/tmihuOyhRDrhRBrhRCrgmV4PFDhcAm9xWyJsCUKhSJURJXQA/fjmg7QwyOATUrZA7C512vifCnlACnlkDrYGLfYHXYAEkzqxUuhiFWiRuiFEGnAOGCWz+bLgTnu5TnAFUG1TEGFs4JEU6LqjFUowkiuYbA4DKUYPERTjP4V4CEg1WdbOynlPgAp5T4hRNsajpXA10IICcyQUs70t5MQYjIwGSA9PT1As2Ibu8OuwjYKRRjJNQze8inFMCmEpRg8RIVHL4S4BCiQUq6uYxsjpZSDgIuBe4QQ5/nbSUo5U0o5REo5pE2bNnVsKrawO+wkmlVHrEIRLnZXK8WwOwwFzqJC6IGRwGVCiGxgHjBaCPEucEAI0QHA/bPA38FSynz3zwLgc2BYEOyOCyocFcqjVyjCSFd3KQbhzr/vGob8+6gQeinlo1LKNCllBnAN8J2U8gbgS+Am9243AV9UP1YI0VgIkepZBsYAG4Jke8yjQjcKRXhJ1zQm2WxkPftsWMI2EF0xen/8DfhICHErkAuMBxBCdARmSSnHAu2Az92diQnA+1LK/9XP5PjB7rSrHHqFIsjkGQY5uk4Xq5U0P0IezFIMgRB1Qi+l1AHdvVwIZPnZJx8Y617eBfSvr5HxigrdKBTBJc8weNens/UGm82v2IeTqAjdKCKHCt0oFMElp1pna06YZ5PyhxL6OEdl3SgUwaVLtc7WLlFQ7CzqQjeK8FLhVKEbhSKYpGkaN9hsp4zRhxvvDFMhHBiphD6KUaEbhSL4pGlaVAi8BxW6iXPsDpV1o1DEOkro4xyVdaNQxD6emeSU0McpqjNWoYh9yivLgdCWI1dCH8WoGL1CEfuU2EsQCFISUkLWhhL6KEZl3SgUsU+JvYQmliYhzbpRQh/FqM5YhSL28Qh9KFFCH8Wo0I1CEfuUVCihj2tU1o1CEfsojz7OUaEbhSL2UUIf51Q6Kxv8xODbDIP/TJ3KtjDNv6lQNDTCIfQNW0XigIY8Mfg2w+C5rCwq7XYSLBYet9noWW3o+Q7DYIuuk9qqFScKC+lptdI9ioanKxSh5rj9OF2bdw1pGwELvRDCDKwC9kopLxFCtAQ+BDKAbGCClPKIn+MuAl4FzLgmJPlbEOxWNAA26TqVdjtOh4NKu51Nul5F6HcYBn/PyqKyvJwEpxMhBAkWCw98/70Se0XcEG2hm/uBzT7rjwA2KWUPwOZer4L74TAN18TgvYFrhRC9626uoiHR22olwWLBZDaTYLHQu1pJ2C26ToXdjnC6an0gJRXl5fw0d274jVUoIkTUCL0QIg0YB8zy2Xw5MMe9PAe4ws+hw4AdUspdUko7rsnFL6+ztYoGRU9N43GbjfHPPus3bHOG1UqixYJb5t0VP6AirFYqFJGlxF5C48TGIW0j0NDNK8BDQKrPtnZSyn0AUsp9Qoi2fo7rBOzxWc8DhvtrQAgxGZgMkJ6eHqBZiminp6adJPAeMjWNh2w2fpg7l+9mzUI4HMjERM6ZODHMVioUkaHSWUmFs4KUxNCVP4AAhF4IcQlQIKVcLYSw1vL8/noSpZ9tSClnAjMBhgwZ4ncfRcNhl2GwXdfpYbXS7RTx9kxNI1PTOHviRJbOnYsZV2eOQhEPlFWWAYS0zg0E5tGPBC4TQowFkoGmQoh3gQNCiA5ub74DUODn2Dygs896GpBfX6PjCSkb3jNvl2Hwmk+2zX022ynFHlzivmrOHCrtdpbPmcODNpvqkFXEPKUVpQAh9+hPG6OXUj4qpUyTUmYA1wDfSSlvAL4EbnLvdhPwhZ/DVwI9hBBdhRAW9/FfBsXyOKChplZud2fbeCZg3h7ABMxbqx2zNQombVZEF/sMg5VTp7IvhsZkeDz65ITkkLZTnzz6vwEfCSFuBXKB8QBCiI640ijHSikrhRD3AotwOW1vSik31tdoRXTTw51t47DbMVss9AhgAuZe1Y7pFQWTNiuih32GwWdZWd7vx5U2Gx1i4I2vtNLt0UdB6MaLlFIHdPdyIZDlZ598YKzP+gJgQX2MVISXbMNgp67T3Wolow7/TN00jftstoBi9B66axoP2mxs1XV6qUFTimrk6ToOnze+PF2PDaEPU+hGjYxVVCHbMJjuE1+/02ars9gHIvC+dNc0JfAKv6RZrZh93vjSYuSNryGEbhQxyE6fWHml3c5OXa+T0NeXjYbBWl1ngNVKHyX+cU8HTeNKm408XSfNaqWDpnHAMNiv67S3WmnXQL8jURm6UcQ+3d2xco9H3z0CntNGw+CBrCwq7HYSLRZestmU2CvooGnecM0Bw2BhVhZOux2TxcLFNluDFPtwefSqeqWiChmaxp02Gxc/+2ydwzb1Za27NILT4aDCbmetysBRVGO/ruN0v3k67Xb2N9DviN1hB0I7MTgojz7qkf7HlwWNPMMgW9fJsFpJc4t6hqZFROA9DHCXRvB49ANiJB6rCB7trVZMFovXo2/fQL8jlc5KgJCXI1dCH8UIvwOLg0eeYfCOT8rajTabV+wjSR9N4yWbTcXoFTXSTtO42GZr8DF6p3RVejKJ0AZXlNDHONmGwQ5dJ9NPqmR2tZS1bF2PCqEHl9jXRuBzDINduk43q5UuUXINitDSTtMarMB78Ix8V0KvqDPZhsEbPqmSd1eLuWdUS1nLqMPr707DqHXu+3b3ZCNnWK30CMI/ao5hMMvnOm+z2ZTYK6pw0CdDp00UfTeUR6+oNzuqlRXYUS1VMk3TuNFmOylGHyg7DYN/+AhsIPVpthsGL/gc87DNViex32YYbNJ1elut7K2WErpL15XQK7wcNAy+8QlR/sZmixqx9wh9qMudKKGPIXJ9OlbTNY3MamUFMv147GmaVkXgaxMC8Vef5nRCv6XarFNbdJ0emuadUvAMq5XMarNQed4YPNu3GQZ/9XlYTH7llSopod0aaMecIjTsd4co8cnQiRah9yRbKI9eERC5hsHbPl7Lze4wzd02W40x+urUNgRSl/o0Z1TL0z/DamWHYfCiT978n2w2Mt3iX/2NIVPTTpqi8FBhIbfZbCpGr/BLe3eIMhozdLwefYgTL5TQRzmBlin217Ga7k6TDDRVclctQyB1qU/TQ9N42GarEqOfP3UqFT7tbtF1MjUNY+5cHGVlSCm9bwyZmuadotDzAOjtFncl8Ap/tNE0fuOToRMt3jz8KvRmU2hnYVBCH8XUJm4XjI7VbtUENJAQSF3q0/TQtCpxec+Ugr5e/k7DYNlbb2GSEgEIs9n7xtBT03jCZvPG6GuawUqh8NBG06JK4D2ozlhFrUjXNG726VhN1zRyfeLt6QF8ybtoWkRCIJmaxp98vPxMTWPh1Kk4Kl2DSYQQnHvLLVVi96eaolChaCgooVfUmnRN8wp6rmEw2ye+favNFrDYRyIE4plS0EPPavF/Tc0jq4hBlNAr6sUan/i2J95eXeizfTz+cJc88JdN40t3TeMPNhvbdJ2efuL/uwzD+7valkNWKKKFqBF6IUQysARIcu//iZTyaSFEf2A60ATIBq6XUhb5OT4bKAYcQKWUckjQrFf4JdcwWFstvl093p5tGMz0TVH0U8DMdwISoEr2zh6fVE7Au9w5ANH1ZNl42vZk2VSnpvj/LsPgVZ/j7w9gTlqFIhqJpqybcmC0lLJECJEI/CCEWAi8BjwopVwshLgF+BPwZA3nOF9KeSg4JitOR7auU1FRgRNXedKht9xykjfvL8PGV+izDYMZbjE1JyTgkBKHw0GCxcLvXnmFb6ZMwWG3YzKbQQiclZWYLRYm2mynFXt/+ff+hL4mtlU7fpuuK6FXNEjCVb0ykMnBpZSyxL2a6P5IoBcuTx/gG+CqkFioqBWrZs5k1cyZOJ1OHIAdaDdw4En7dbNaMSckgBCYExJO8virT0DiqKjwCuumTz/9NZWzouKktM7T4cm/N5nNdZoftme143tGUV60QlEbyivLAUhKSAppOwHF6IUQZmA1kAlMk1IuF0JsAC4DvsA1MXjnGg6XwNdCCAnMkFLOrKGNycBkgPT09FpdRCxTmzLFq2bOZP4dd+DA9Yc1ARVCcLywEKg66lUAJikxuX9Wf3H0nYDE49E7HQ7MFgu9r7qKvUuX+vXoA0nr9GTZnCpGfyq6aRr3+8TvlTevaKhE1VSCUkoHMEAI0Rz4XAhxFnAL8E8hxFPAl7icR3+MlFLmCyHaAt8IIbZIKZdU38n9AJgJMGTIkNAWYW8g1DZut3b2bCS//lHNgFNKyo8ePWnU66CbbsLpcCDcAl59cFSGpnGHzVZjjL5D3751jtHDyVk2taUuc9IqFNFGuaMckzBFVz16KeVRIYQOXCSl/AcwBkAI0RMYV8Mx+e6fBUKIz4Fh/BryUQSRJh07eh8NAterlBn48eWXKSsqqhKKAU47OKr6qFrf5c6aVkXUAxX4QNjtU1q5qxJzRQxTVlkWcm8eAsu6aQNUuEU+BbgAeEEI0dYt3ibgCVwZONWPbQyYpJTF7uUxwDPBvQSFh5EPPcS2r75COhzegI8TkJWVHN+/v4qwD5o4kUETJ9ZrcFQoasDvNgym+bx53GOzKbFXxCzlleUkmUMbn4fAPPoOwBx3nN4EfCSlnC+EuF8IcY97n8+AtwCEEB2BWVLKsUA7XKEeT1vvSyn/F+yLULjorGlMWrqUtXPncnDTJnb/8APS6QQp2bpgAZe+9hrHCwurCHNdBTpUNeD9lVZWQq+IVcoqy0LeEQsBCL2Uch1wUtqGlPJV4FU/2/OBse7lXUD/+pupCBTfkMp/7rqLFTNmgDsOf6KwkPMffTQo7dS2AFqgBFJaWaGIFexOe1g8+tAOx1LUmd1HdlNsL67XOQZNnEhicjLCbPbG4XMNg8VTp5JrGPU6t6cAmu+5g0FXTeMem42xzz6rwjaKmKfCURHyHHpQJRCiAikl3+76loU7FmJ32Nl1ZBcLdyys93nTNY1bfYqUAbzpE265JcD6N/4IVQE0T1mGHhEoyxAt7PQp71DbyqCKhoXdYSfRnBjydpTQRxApJbuO7OLx7x7nw40fkpyQTHJCMg6ngzsG30GvVr0Y22NsvdrwLXS2eOrUKuGW3X7q39SGYBdA8x2Nm2CxcIefsgyxzk7D4CWfe/BAANMzKhouFU7l0cc0xeXF9HmjD3uK9gBw+6Dbee3i10LaMdO1Wr35rlEW/64+GndntbIM8YC/8g5K6GMXu8NOokl59DGLxWyhT9tfhX5019Eh731P1zRusdnYret0DbBGfTjpXu1B1D3KHkThoHp5ZlXeIbapcFSEJXQjAp2qLpwMGTJErlq1KtJmhJzj9uM0mdoEgLV3rGX74e0M6zSM9GbRWQIi1zDq9JCoaeJvf/hWzIw3b95zn1JbteJEYaGK0ccB5711HmaTme9v+r7e5xJCrK6pOrDy6CNIY0tjnhv9HI9/9zgDZgwAILNlJtvv2x5Zw/yQaxi85RM7nhRgR25NE3/XRG3muI0l/N0nJfKxT4WzIiwjY1V6ZYR57NzHsE20MWX4FABapbSKrEE1sLta/Hx3AFUqAbbo+kkTfytORt2n+CRc6ZVK6COMw+nAKZ2sPbAWgAu7XxhZg2qga7W8+UA7cj0Tf5vcx52hYs5+UfcpPlHplTGMlJJ9Jfs4f875HCg5wLHyY1jMFp449wkeP+/xSJvnl3RNY1IdOnL9TfytOBl1n+KTCmeFyrqJRfYW7SXt5TTvukmY+Ne4f3Fd3+tomtQ0gpZVZbdhsN09cMkzOtU3J7821LckcbxQ/T6peXFjn0pnJWaTOeTtKKEPI0tzlnLe2+d51xNMCRx5+AhNLE0iaNXJ7DYMXvPpeL1PlSIIO2pe3PhAShnyicFBxejDypKcX8vwd2/RnaJHiqJO5AG2Vxu0sz1EHYO7DYOvp05ldz3r7sQi/gZOKWIPiQz5xOCghD6sXJR5kXd555GdNHq+EXfNvyuCFvmnR7U5WXv46RjMMQy+mzqVnDqKtOetYf6TT/JaVpYS+2qoeXFjl0pnJYt2LOLWL25lb9HesHj0KnQTRga0H8C/L/03P+/7mfLKct5c+ybTV09nT9EerjjjCib2nxiWVKvT0VXTuM9mOylG7yHHMJjpE1aYXIda9P7eGlR46FfUvLixRaWzkjX71jBvwzze3/A++0v20yypGRf3uJg7h9wZ8vaV0IcRs8nMbYNu8673btOb+dvns/3wdm7/6naeWfwM08ZO49Jel0bQShddNY2umkauYfDlXXchgYETJ5KuaX5r0tRW6Bu3aoUwmUDKGt8a4h01L25s8PqK13n424c5UXGCRFMiY3uM5cZ+N3JJz0vCMukIBFACQQiRjGuO1yRcD4ZPpJRPCyH645o+sAmQDVwvpSzyc/xFuCYoMeOaeepvpzMqXkogeJBS8pLxEg9+8yDpzdLJmZITaZMA12jYN61W7xyz5qQkbv3+eyTUy6PfbRi8npVFRXk5JpOJ8dOmMXLy5BBdhUIRWc54/Qy2Fm5lQPsBzL92Pp2adgpJO6cqgRBIcKgcGC2l7A8MAC4SQowAZgGPSCn7Ap8Df/LTsBmYBlwM9AauFUL0rtNVxDBCCLK6ZQGQeyw3wtb8Srau46io8K473CNiu2gak202Lnz22XqFbXBPc3i8sDDYpisUUcOX135JerN01u5fS9rLacz6eRZO6QyrDacVeumixL2a6P5IoBcuTx/gG+AqP4cPA3ZIKXdJKe3APODyelsdg7RMaeldXp63PIKW/EqG1Yo58dfBHGafEbFdNI3Rjz5ap3r0gXT2KhSxQs9WPcm+P5tFNyxiZOeR3P7V7Zw/5/ywOnUBVa90e+argUxgmpTyYSHET8ALUsovhBAPAH+RUqZWO+53wEVSytvc6zcCw6WU9/ppYzIwGSA9PX1wTk50hC/CxU97fmLkmyO96zf1v4mB7Qdy5ZlX0rlZ54jZlWsYrJ07t0qMviZqM8DH34AshSLWkVIye81s7l1wLyZh4tuJ33J257ODcu5ThW5qVaZYCNEcV5jmPqAS+CfQCvgS+L2UslW1/ccDF1YT+mFSyvtO1U68xeg9fLn1Sx6zPQbAoROHOHD8AE2TmjJ50GT+bP0zjS2NI2xhzewyDF72idv/QQ3wUSj8IqXkzTVvcttXt3FjvxuZ+9u5QTlvfWP0vgYeBXRcXvoWKeUYKeVg4ANgp59D8gBfdzQNyK9Nm/HEZb0uY8PdG9hw9wb2P7ifDXdtoKi8iH8Y/2DdgXWRNu+UqAE+CsWpsTvszP55NgNnDOS2r26jXeN23D7o9rC0fVqhF0K0cXvyCCFSgAuALUKItu5tJuAJXBk41VkJ9BBCdBVCWIBrcHn/igA4WnbUuzwibUTkDAkANcBHoaiZovIihv17GLd9dRtO6eTNy94kZ0oO53Y5NyztB5JH3wGY447Tm4CPpJTzhRD3CyHuce/zGfAWgBCiI640yrFSykohxL3AIlzplW9KKTcG/zJiCyklT37/JM8tfc67TYjQD5OuD900jT+oAT4KhV/yivK8b+X92/dnWd4y1u5fS3JCMo0SG9G3XV9Gdh5JuybtQtK+mkowCll3YB39p/f3rs++bDa3DLwlghbVHlV5UaGoyoLtC/j7j38n91gupZWllFWWeT/gqmT71HlP8bT16TqdP2idseEi3oU+4ZkEHNIBwP+u/x8XZgZ/MpI8wyBH1+litZIWZCFWlRcVisA5UHKAKYumMG/DPIZ1Gsby2+qWXh20zlhFePjbBb8OHr7ovYsosZecYu/ak2cYvJeVxeInn+S9rCzyglxQTHXMKhSnJ/toNnf/9266/bMb8zbMY3in4cy6dFZI2lJCH4U8ePaDyKcltw68FYCZq2cG9fw5uo7DR4hzgizEqmNWoTg9d//3bv616l9kdc1izR1rWHbbMvq26xuStlRRsyhmfO/xzF4zmye+e4KyyjJ+P/z3Qalf38VqxWyx4LDbMVssdAmyEAe78mK2YbBT1+lutZJRx3OpAVqKSJB7LJelOUspsZfQulFrWjdqTatGrWjdqDV3DL6DRTsXsWb/GjqmdgypHSpGH+VsKNhA33+5nvLDOw1n2W3LgnLeUMbofckxDHbpOt2s1jqVS8g2DKb7xPvvtNlqLfZqxixFsKlwVPDTnp/4ZNMn5BblUuGoQAhBq5RWmE1mDpQcYM3+Newv2R/Q+T6b8Bm/PfO39bLpVDF65dFHOTlHfy0FsXxv8GrgpGlaQAJfH286xzCY5SOwt9WhAJq/ksi1tUPVvlfUl6LyIpblLeOnPT/x454fWZa3jBJ7CY0SG9GjZQ8SzYlIKdlYsBGndNIipQVjuo9hSIchnNflPNo0bsOhE4dO+pRWlNIxtSPjeo4Lqf1K6KOcsT3Gcvug2/n3z/+mcWJ4SyBkGwYzfIT6jlp607uqifSuOtSt7+6O93ts6F6HMJOniJonVKWKqDUM7A473+76liU5S8g9lovFbKF9k/Z0TO1IhaOCwtJCDp04RGFpIYUnCjlcephj5cc4UXGClIQUUpNS6dGyB0M7DuXeYffSLLlZrdpenL2YhTsW8t3u71hfsB6ndGISJvq168fEfhPJ6pbFmO5jAg6nhjo8cyqU0Ec532d/z7wN8wCYNnZaWNuurzfdrZpId6uDwGZoGnfabPWK0Z9uxixF9PHL/l+4/rPr2XhwI4mmRNKbpWN32Nlfsp8Kp6t0tlmYvTHvVimt6N6yO82SmtEosRGllaUcKzvGlkNb+HLrl7yx6g1ev/h1rjjjilMOPlx/YD3//vnfvLvuXY6UHSHJnMQ56efw5HlPMrLzSIanDadpUtNw3YagoWL0UUxReREDZwzkRMUJlt26jC7Nu4S1/fp69FD/GL0i/jh04hDd/9mdlIQUXh/7Opf0vITkhGQAnNLJoROHSDIn0TSpaUAjxlflr+K2L2/jlwO/MKTjEC7teSn92/XHYrZQbC/mcOlhVuev5sc9P7L50GYsZgtXnnkl1551LRd0u4BGiY1CfclBQQ2YinIOlx5mcfZidh7ZyZ5je9hXso/9JftZlb+Kssoypl8yncmDwzsD0x7DIFvXKTl6lL1r15I2YACpzZsHvfM2x6cPoKE9CHYbBjt0nUz1lhBUvtr6FZfNu4w7B9/JtHHTgjJ5doWjgtlrZjN91XTWHViHpKruNU9uztmdz2ZMtzHc0O8GWjVqVcOZohcl9FGMbZeNy+ddzvGK4wA0sTShU2on2jdpT9+2fZk0cBKDOgwKq017DIM5WVnYy8uxO50gBEhJI5MJS1ISN9hsQRH7YEwyHil2GwZv+Nh+t8rkCRpllWVc++m1/GfLf7hn6D28Pvb1oJ6/qLyIrYe24pAOUi2pNE1qSqemnYLyQIkkKusmCimrLMP6tpXle5fTKqUVC69fSL92/WrVYRQqst0DqpxO93Rnbmeg0unE7B5gFQyhD8Yk4/WhPvV4dlTL5NmhMnmCRnJCMp9N+IxRb4/i651fB/38TZOaMrTT0KCfN5pRQh8h1h9Y702XXDV5FRnNMyJrkA8ZngFV5eWueV1NJnA6KQNMZnOtBlidKkZf24yabJ9z1XXglIddhsErPh75lFrW48mslsmTGeJMnmyfMFGGprHLZwBYLNYREkJgNplp07hNpE2JCZTQR4BlecvQZv/6z9mucWhKk9aVzprGTTYb2bpOcqtW5K5Zgz57NuWVlZQIQXmA5zldHr1nkvFAYvTZfsI89RF7f/V4aiOYXTWNu222sMTos6uFia545RU+njKlygCwWBT7M1ufydxf5rL10FZ6te4VaXMaNA07KNUAWZ63vIrIg6u4UbTRWdM499FHGTp5Mo3S012xeilxVlYGXKTMXx59dQKdZDyQc52K3YbBN1OnsttdwC0Y9Xi6ahq/efTRkIdsqoeJ1n766UkDwGKRe4beQ0piCiNmj2B5XvAGC8YjyqMPE8Xlxby19i3u/9/9ADx2zmOMyhhFtxbdyGyZGWHrTk3PamGKQEUxGHn0wTjXbsNgmo9HfI/bA54SBROl7DAMNus6Z1qtZNZgQ/Uw0YCrrmL70qUxPwCsT9s+rLx9Jda3rdy78F5W3r4y0iY1WE4r9EKIZGAJkOTe/xMp5dNCiAG4pg9MxjVR+N1SyhV+js8GigEHUFlTr3Cs89T3T/HK8lcA+GT8J1zV+6rIGlQLumsaD/iIYvcARbGLpnGbzRaUPPoMd5inLjH6mjpOu7k/kWKHYfCCzwPoYZvNr9hnVAsTZWga7fv2jekYvYfSilKaJTcjrygv0qY0aALx6MuB0VLKEiFEIvCDEGIh8AzwFynlQiHEWODvgLWGc5wvpTwUFIsbKJf0vMQr9Au2L+CKM67AbDJH1qha0F3TAhZ4X7poWtAyaTI0rU5x+XB3nAbKZvcDyOkOR23W9Rq9+urXHumHVKgoPFHIe+vf46c9P/HTnp/YU7QHi9nCjEtmRNq0Bs1phV66Eu09M18kuj/S/fGMBW4G5IfCwIaKUzo5VnYMI89g4faFzFj96xf1o00f8dKFL0VFKmU80FXTuKeGjtNAQieh4sxq4agzo+QBFCnWHVjH6DmjKSwtpHPTzmidNR7s/CDXnHUNbRu3jbR5DZqABky5JwZfDWQC06SUDwshzsQ16bfA1al7tpQyx8+xu4EjuB4MM6SUfmfREEJMBiYDpKenD87JOelUUY3D6WDm6pm8svwV8oryOFFxwvu7RFMiE/pM4Nz0cymtLOXSnpfSvWX3CFqrgMBDJ6G2IVIPmmhASsnOIzuZv20+f9b/THJCMl/f+DX92vWLtGkNjnoPmJJSOoABQojmwOdCiLNwifIfpJSfCiEmALOBC/wcPlJKmS+EaAt8I4TYIqVc4qeNmcBMcI2MDcSuaOKFH1/g8e8e964/ed6TNE1qyqAOgxiRNqLB1MuIJ2oTOgkVmZoW0wLvlE5W5a9iWd4yso9mc9x+nJKKEkrsJRQcL2B74XYKSwsBGNVlFLMumxX1yQkNkVpl3UgpjwohdOAi4CbgfvevPgb8TnYopcx3/ywQQnwODMPVuRtTDGw/kPRm6eQeywVgTPcxnJN+ToStiix57no5GT71cXINg7Vz5wIwYOJE0iMocip0Elo+WP8Bj333mDd9uHFiY5pYmtDY4vrZplEbLu91OSPSRjAyfSS92/SOrMExTCBZN22ACrfIp+Dy2l/AFZMfBejAaGC7n2MbAyYpZbF7eQyuTtyY4+IeF/Pc6Oe48fMbefK8J9HSYtdLC4Q8w+CdrCxvB+iNNhtO4C2rFYfdDsCat95i0vffVxH7XMNgt67T1WoN+UMgU9N42GaL69BJqHh12atMWTSFwR0G84z1GbK6ZUW0Hnu8E4hH3wGY447Tm4CPpJTzhRBHgVeFEAlAGe74uhCiIzBLSjkWaIcr1ONp630p5f+CfxmR5UTFCZ5f+jwv/vQi6c3SeezcxxpURk0o8NTL8aQ0Zus6TsBRUeHdx1Fezi9z53oFPdcweMvn4TDJZjul2O81DHJ1nXSrlU51FOlYD51Egi+3fskfFv2BcT3G8cU1X8T9/0I0EEjWzTpgoJ/tPwCD/WzPB8a6l3cB/etvZvRxoOQAq/JXsfnQZl5f8To5x3K4od8NvPibF721s+MZb70ct2hnWK04AXNiotejF7i8+gETJ9JZ09hd7eGwW9drFPq9hsE8n4fCNTZbncVeETyklDz87cOc2eZMPh7/sRL5KEGNjK0l2UezeWbxM7y77l3vTDeDOwxmzhVzGJUxKsLWRQ9pmsaN7no5vjH6SbrOwilTyF+50ltSYbeu01nT6Frt4dD1FDHz3GoPhVxdV0IfBRh5BlsObeHVi14lJTEl0uYo3CihrwX7ivcxes5o8ovzmTx4MteedS2ZLTNp27htQDPdxBv+JiBP1zTGvvIKb/t44x5BT9c0JtlsAcXo06s9FNJVR2rEKThewLWfXkvnpp25vu/1kTZH4YOaeOQUrNm3hrfWvkVa0zQePPtBpvxvCq+veJ0fb/kRrbPyHuuCp7O1UatWlBYW0tVqpXMdPfFgxOgV9UdKyaebP+X+/93P4dLD/DDpBwZ3PCmqqwgxauKR03Cs7Bi3f3U7K/NXsufYHm4fdDt3D72b0XNHc7TsKABDOw7lcOlhJJKBHU7qslAEQK5h8KbPAKVbbLY6izxAJ01TAh9kyivLyT2Wy56iPeQey6XSWcnQjkPp265vlRmYCo4X8GPujyzNXcqinYvYdHATA9oP4LMJnymRj0LiXui/3vk1F757YZVt01dPZ8bqGTRPbs6lPS/lq21fcfMXN5N7LJdLe16qOlvryO5qpYZP1dmqCD9vrHyDP33zpyqjun154YIX2F64naW5S9lauBVwzQalpWlMGT6FSQMnkWCKe0mJSuLyr3L7l7cza80s3vntOzxqexQAgeDzqz/nig+vINGUyINnP8h9w+7DYrbwtP40eUV53DfsPu4Zek+ErW+4dK02QOlUna2K8LJm3xruXXAvozJGMWnAJNKaptGlWRckkhmrZvD2L2/z8LcP0zy5OSM7j2TSgEmc2+VcBncYTFJCUqTNV5yGuIzR3/LFLby19q0q2z6d8ClXnnkldocdkzApzyREhHNAlCIwjpYd5a7/3sW8DfPYdu82erTq4Xe/I6VHaJbcrMFPoh2rqBh9NVISXGlf1/W9jg5NOvDkeU96K0lazJZImhbzpGuaEvgoYXvhdl5f8TpvrX2LYnsxT496ukaRB2iR0iKM1imCSVw+moenDQdgQ8EG+rXrh0M6ImyRoiHzzi/v0P2f3Wn8fGP6vNGHZxc/y55jeyJtVo1kH83mkvcvoefrPfnXqn9x+RmXs+aONfzZ+udIm6YIEXEZugH4eOPHPKU/xZZDWxAIRmWMokVyC45XHOev5/+VoZ2GhrR9ReyQOjWV0opSftf7d+wv2c/inMUIBIM6DOKCbhdwcebFnNvl3KgIeazYu4Ix74xBIvmj9kcmD55M+ybtI22WIgicKnQTt0IPrhKqxh6DRTsX8eySZ73bx/cez0fjPwp5+4rYYNz741iwfQEASeYkHNJBpbMSgARTApXOSnq36c30cdM5t8u5kTSVMe+MYUPBBn645Qe6tegWUVsUweVUQh95FyNCHCk9wr0L7uXlZS+Tc+zXSU5apbTixd+8GEHLFNFGpbOSr7Z+xSvLXmHaimlsKNjg/d2Ggg30a9uPM1qfQZI5iXJHuVfkx/cez+GHDvPub9/luP04o94exU97forUZQCwt3gvZ3c+W4l8nBE3nbFSShbnLGbl3pWU2Et4b/177Dyyk46pHSk8UUhW1yz+qP2RizIvUuUMFF4cTgej54xmae7SKttHdh7JxZkX88ySZ3A4HbRIaUGCKYFyRzlmYWZUxiheuOAFUpNSub7f9RwpO8J9C+/j4PGDEboSF20atVETbcchcSP08zbM47rPrjtp+4GSA6y9cy1ntT0rAlYpoh27w87a/WsBV/G6oR2H0jy5OZ9s/oQnvn+CXq16od+se+PcDqcDkzCd5CzMXD2TQR0GcVmvy8J9CVXo06YPs9fMRkqpHJo4Im5CN75ztHZM7Uh6s3QAHNLBkpyYm/BKESRSElNYdtsyHjr7IYrtxUxfPZ19JfvYeu9Wdv5+J+vvWl+lM9NsMvsV0MaWxlQ4KiKa4bXz8E7eWPUGvVr3UiIfZwQyw1Qyrqn/ktz7fyKlfFoIMQCYDiQDlcDdUsoVfo6/CHgVMOOakORvwTP/9Hyz8xtmr5lNYWkhwzoNQyDIOZbjnfLvlgG3cMfgO8JpkiKK2Ve8j91Hd3Os7BjHyo9xrOwYB08cpOBEAUdKjwAwIm0EJmGqVZz7/uH3c+2n19L3X30Z33s8Y3uMZWjHoWGt1/7XpX8F4L0r3wtbm4ro4LRZN8L16G8spSwRQiQCP+CaK/YZ4GUp5UIhxFjgISmltdqxZmAb8BsgD1gJXCul3HSqNoOZdTN45mB+3vdzlW0T+09kYPuBXNDtAhWyUXj5Zf8vDP33UO88A760btSaC7pdwKQBkxjTfUydzv/xxo95dfmrGHkGTumkdaPW3NjvRsZ0H8OItBE0T25ezyuombLKMnpP680Zrc9gwfULQtaOInLUa2SsdD0JStyrie6PdH+aurc3wzWHbHWGATvcM00hhJgHXA6cUuiDyQsXvMAj3z7C6n2rAZg8aDIzLp0RruYV9eRExQmSzElh8XwX7lhIhbOCTyd8SsfUjjRLakaz5Ga0TGkZlEJ24/uMZ3yf8RwuPcw3O7/hk82f8NqK13h52csA9G7Tm95tenO49DB7i/ZyuPQwpZWlJCck075Je/q3689lvS7josyLaJrU1G8bDqeDwtJCDh4/SMHxAg6eOMj6A+uZu24uucdy+ftv/l7v61A0PALqjHV75quBTGCalHK5EGIKsEgI8Q9csf6z/RzaCfAdIpgHDK+XxbXkgm4X8Nblb9F/en/6tevH9Eumh7N5RS05dOIQn2/+nGV5y1i+dzmbDm4iKSGJ3m16M6LTCJ7Lei5knq9TOgG4pOclIS2F0TKlJVefdTVXn3U1xeXFrMxfyU97fsLIM1h/YD0tU1rSt11fWqe0JiUxhbLKMvKK8vh659e8t94Vdnno7Id44TcvUHiikDvm38HGgxs5ePygt5S2LwJBVrcsZl06i990/03IrksRvQQk9FJKBzBACNEc12TfZ+GaDPwPUspPhRATgNnABdUO9dfj4zdWJISY7D4n6enpgVkfIBnNM7jmrGv4YMMHLM5ZjDXDGtTz14eth7Zy039uIvtoNv3b9+e1i1+jZ6uekTYr7Ow+spuXjJeYvWY2pZWltExpyYi0Efyu9+8osZewvmA9M1bPYNfRXfzn6v+EpGKicH9dHU6Hq0cpDKQmpTK662hGdx192n0dTgeLdi5i3Pvj+PtPf8ditlBsL+bTzZ8yrsc4zs84nzaN2tCmcZsqPzs17UTLlJZhuBpFtFKr9Eop5VEhhA5cBNyEK1YP8DEwy88heUBnn/U0/Id4kFLOBGaCK0ZfG7tOxcaCjQycMZAKZwUCQaIpMVinrjf7S/YzZdEUlu9dznV9r+P99e/T6/VeyKdrvvyjZUeZ/bOrcznnWA4Xdr+Q0grX633rRq3p3rI7PVr2aBCTMpdWlLJ632qmrZzGRxs/wizM3NDvBu4ffj/92vU7KTPk9RWvc9/C+0h+Lpnjjx2nUWKjoNqT2TITgAcWPcA/xvyDxpbGQT1/fTGbzIztMZZjjxzjrv/e5e1cbZTYiI/GfxT0+6GIHQLJumkDVLhFPgWX1/4CLsEeBejAaGC7n8NXAj2EEF2BvcA1wMnJ7EHmSOkRJv5nIktyltCrVS8qnBV0adaF/173X/q07XPKY4vLi5n7y1x2HN5B60atGZk+klFdRgU9HW3+tvlc+sGlADw96mkePedRthduZ2X+Si5890Im9puINcNKp6advMccKzvGlR9eyffZ33u3vb/+/ZPO3cTShBFpI5g8aDKX9orcRCmFJwpZvnc5K/auIL84n0MnDnk/R8uOUnC8AId0kGpJ5Y/aH7l/+P1Vrrc69w67l/zifKb+MJVRb4/i4/Efk9E8o9Z2VTorWbl3JQmmBFKTUkm1pNLE0oRzu5xLz1Y9mb56Oo0tjfnHmH/U4+pDR9Okprx35Xs8PPJhVuxdwdmdz1YirzglgXj0HYA57ji9CfhISjlfCHEUeFUIkQCU4Q67CCE64kqjHCulrBRC3AsswvUy/KaUcmMoLgRgSc4SjD0GJfYS5m+bD8DK/JUA5BzLwbbbVqPQSyn5YMMH/OmbP5FfnE+jxEbemXa0NI05V8w5ZQnXms65++hu1h1YR3JCMuekn0MTSxPANYAG4IozruCpUU9hEiaMWw1eWfYKL/70IjfsvAFweZmjM0ZzuOww87fNp6yyDIBt927jcOlhWjVqRaPERpRVllFwvICth7ayet9qPtv8GRM+mUDz5OYM6TiEni170q9dP87ufDZ92vYJWYGtvUV7eXvt23yw4QM2HnT9qU3CRNvGbWndqDWtG7Wmd5vetEhuQYfUDgxoP4CsrlneMtGn4/ms5xmRNoIbP7+RgTMG8vzo57lt0G2cqDjBD7k/sDhnMQu2L2B/yX6aJzenU9NOXHvWtdw55E7A9fAZ9fYor23+EAj6tu1b/5sRYvq160e/dv0ibYaiARBTRc3OeP0M7xRnNbHx7o30btO7yrb84nyu+eQaluYuZXCHwbx28WtonTWO24/z/vr3eeDrBzhRcYK/WP/CfcPuo7GlsXdikh2Hd/D97u/55cAvrC9Yz87DOzEJE8kJyeQV5VFaWeptp0VyC1bevpLuLbvzy/5fGPv+WPKL8+navCu3DryVu4feTYuUFjicDtbsX8PSnKXYdttYkrOE5snNuaTnJdw84GaGdhx62jcMh9PBd7u/Y96GeawvWM/Wwq0UlRcBrmHwF2ZeyI39biSra1bQwjy7juyi//T+lNhLOK/LeVzU/SK0zhpDOg7xPuCCxa4ju5j0xSSW5Cwh1ZLK8YrjOKUTi9nCOenn0KtVL46UHWHehnmYhZmyJ8pIMCXw8caPmfDJBJ49/1n6t+tPsb2Y4vJiiu3FJJoS6dO2D0M7Dg34waNQRAtxU73yq61fcf1n11NsLwZcRaUOHD9AeWU5BccLyGyZyUfjPzopa+P5pc/z+HePc+1Z1/LOb985Sfjyi/O5c/6dfLXtK++2BFMCTun0ZmqkWlI5q+1Z9GrdC3ClBaalptGzVU96te7F1Z9cTcHxApbcvMRbwbC8spzPt3zOP5f/EyPPYEKfCXz4uw9rfd2BIKVk55Gd/JD7A7bdNuZvm8/RsqN0Su3EhD4TuLTnpZyTfg6J5rr3YXy/+3tGzx3NS2Ne4g/aH4JovX+klCzYvoD52+bTrkk7RnUZxYi0EaQkuiaWeW35a/z+f79nyvApvHyRK4VxwfYFjHt/HJ+M/4Srel8VchsVinARNzNMXdrrUg7+6SCTvpjEBxs+oNxRzsWZF9OtRTfO63JejXW3rznrGmasnsEHGz4g+2g2r138WpWZ7DumduSLa77g651fs75gPaUVpZRWlmIWZjqmdiSrWxY9Wvao0cu+5YtbKDhewIu/ebFKmdqkhCQGth+IkWcAcE7nc4J4N6oihCCzZSaZLTO5ecDNlFWWMX/bfOb+MpdpK6fx8rKXSbWkck76OfRr14/myc1pmtSUVEsqaU3TqghoTXyz6xuAsIUThBCM6zmOcT3HnfS7ExUnePCbBwFo07gNT3//ND/u+ZHFOYuxmC1xmdmkiF9iyqMvrSjl4W8fZu3+tSdVG2yZ0pLd9++ucaBJaUUp//7530z9YSrlleXsfWDvaYUtUK779Do+3Pgh15x1De0at6PCUUGRvYjdR3azLG8ZZpOZF3/zIvcOuzco7dWW4vJibLttLNy+kJ/yfmLLoS3eUrseWjdqzaxLZ3H5GZf7Pcemg5sYOGMgV/e5mrm/nRsOs0+JUzoZPWc0i3MWe7f1a9ePC7pewD3D7lFlehUxR9yEbr7Y8gVXfHhFjb//ZPwnnNH6DM5sc+ZJnZHlleWs3reaZ5c8y/92/I9FNyyq81D36hw6cYjJX01m7f61FBwvICkhiSaWJqQ3S2dIhyE8cs4jtGvSLihtBQMpJWWVZRSVF1FUXsTWwq089f1TrNm/hv8b8388oD1QZf/cY7lc+eGV7Dqyiy33bqFt47YRsrwqUkqOVxzHLMyYTWY1H7AipokboXc4Hby6/FWm/jCVQycO1bhf8+TmaGkaWV2zuG3QbZRVlmGdY2XLoS2Aq5Trdzd9FzWCFQ3YHXZ+99HvWLRzEX+x/oWWKS3JL87HyDOw7bKRaE7kg6s+4Iozroi0qQpFXBI3Qu/B7rCzsWAjjRIb0bNVT0orS1l3YB0JpgQ2Fmzkxz0/8uOeH9l0cBMpCSlIJALB81nPc3Wfq+mQ2iGIVxM7ZB/N5soPr2TN/jWAKw0xs2Um43uPZ/LgyXRp3iXCFioU8UvcCX2g/LzvZ+b+MpeyyjLuGHwHAzsMDHmbscDh0sMctx+nXZN2KhyiUEQJcZN1U1sGdRjEoA6DIm1Gg6NlSktVO0WhaEDEzQxTCoVCEa8ooVcoFIoYRwm9QqFQxDhK6BUKhSLGUUKvUCgUMY4SeoVCoYhxlNArFApFjKOEXqFQKGKcqBwZK4Q4COSEsInWQM3FcKIXZXd4UXaHj4ZoM0SX3V2klG38/SIqhT7UCCFW1TRUOJpRdocXZXf4aIg2Q8OxW4VuFAqFIsZRQq9QKBQxTrwK/cxIG1BHlN3hRdkdPhqizdBA7I7LGL1CoVDEE/Hq0SsUCkXcoIReoVAoYpyYFXohxAAhxDIhxFohxCohxDD39kQhxBwhxHohxGYhxKM1HN9SCPGNEGK7+2eLCNp8vXub5+MUQgzwc/yfhRB7ffYbG2qbg2R32O/1qex2/66fEMIQQmx0f1eS/RwfVfe7FnZH1f0WQmQIIUp97uP0Go6PqvtdC7sjcr+rIKWMyQ/wNXCxe3ksoLuXrwPmuZcbAdlAhp/j/w484l5+BHghUjZX26cvsKuG4/8MPBgt97oWdof9Xp/mO5IArAP6u9dbAeZov9+1sDva7ncGsCGA46Ptfgdqd0Tut+8nZj16QAJN3cvNgHyf7Y2FEAlACmAHivwcfzkwx708B7giZJb+Sk02+3It8EEYbKkN9bU7EvcaarZ7DLBOSvkLgJSyUErpCJNNgVBfu6Ptfkc79bU7Uvf7V8L9ZAnjU/hMIBfYA+zFNTwYIBGYBxwEjgOTazj+aLX1I5Gyudo+O4Gzajj+z7jeUNYBbwItInmva2F32O/1ab4jU4B3gEXAz8BDDeF+18LuaLvfGe7/xTXAYuDcBnK/A7U7Ive7SpvhbjDIf4BvgQ1+PpcD/wSucu83AfjWvTwSeA+X4LcFtgLdwvXHqYvNPscOB9af4tztADOuvpfngDcjea9rYXdI7nU9viMPArtx1TFpBBhAVrTf71rYHW33Owlo5V4ejEtQmzaA+x2o3SG73wFfX7gbDNuFwTF+HScggCL38jTgRp/93gQm+Dl+K9DBvdwB2Bopm31+/zLwWIDnyiCA+GE02B2Je32a78g1wNs++z0J/Cna73egdkfb/faznw4Mifb7Hajdkbrfvp9YjtHnA6Pcy6OB7e7lXGC0cNEYGAFs8XP8l8BN7uWbgC9CaKuHmmxGCGECxuMKO/lFCNHBZ/W3uDyScFAvu4nMvYaa7V4E9BNCNHL35YwCNlU/OArvd0B2E2X3WwjRRghhdi93A3oAu6ofHG33O1C7idz9/pVwP1nC9QHOAVYDvwDLgcHu7U2Aj4GNuP4J/uRzzCzcT2RcGQs2XH9UG9AyUja7f2cFlvk5xtfmd4D1uGKYX+L2IhqA3WG/1wHYfYP7O7IB+HsDut+B2B1V9xu4ym3zL7j6Fi5tCPe7FnZH5H77flQJBIVCoYhxYjl0o1AoFAqU0CsUCkXMo4ReoVAoYhwl9AqFQhHjKKFXKBSKGEcJvUKhUMQ4SugVCoUixvl/gYZq+Pwk4IUAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# ... And where are the OVERUSED tracts?\n",
    "minPlot = 1.2\n",
    "print(\"here is a map of tracts that were used more than {0} of expectation\".format(minPlot) )\n",
    "for t in range(nTracts):\n",
    "    if(tractUse[t] >minPlot and tractUse[t] > 0.05):  #ignore skipped tracts\n",
    "        redd = min(max( 0, ( HDvGOP[t] - 0.5) * 3.0 ),1)\n",
    "        bluu = min(max( 0, (0.5 - HDvGOP[t]) * 3.0 ),1)\n",
    "        plt.scatter(tractCPx[t],tractCPy[t],marker='.',color=(redd, 0,bluu ) )\n",
    "\n",
    "x,y = wholeMAP.exterior.xy\n",
    "plt.plot(x,y,c=\"green\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "id": "19bfa7c8-0449-4182-8ed9-f5a1c1276f03",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "here is a map of 14 tracts that were used more than 1.4 of expectation\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# ... And where are the REALLY OVERUSED tracts?\n",
    "minPlot = 1.4\n",
    "counter = 0\n",
    "for t in range(nTracts):\n",
    "    if(tractUse[t] >minPlot and tractUse[t] > 0.05):  #ignore skipped tracts\n",
    "        redd = min(max( 0, ( HDvGOP[t] - 0.5) * 3.0 ),1)\n",
    "        bluu = min(max( 0, (0.5 - HDvGOP[t]) * 3.0 ),1)\n",
    "        plt.scatter(tractCPx[t],tractCPy[t],marker='.',color=(redd, 0,bluu ) )\n",
    "        counter +=1\n",
    "\n",
    "print(\"here is a map of {0} tracts that were used more than {1} of expectation\".format(counter, minPlot) )\n",
    "x,y = wholeMAP.exterior.xy\n",
    "plt.plot(x,y,c=\"green\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "id": "f3e9bde3-06f6-402d-bce1-62b5631e8db0",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# CORRELATION OF TRACT UNDER-USAGE IN OTHER TRACT'S HOME DISTRICTS vs. being near a boundary\n",
    "fig, ax = plt.subplots()\n",
    "plt.scatter(tractUse,nearEdge,marker='.' )\n",
    "ax.set(xlabel=\"tract use vs. expectation\", ylabel=\"number of unfilled wedges - max = \"+str(nWedges))\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "id": "6bb5c12a-a6a4-4cb1-9026-d227bc11c2ea",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.13819997576197685 = IN std dev of TRACT usage.  Here is its weighted histogram\n"
     ]
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# LET'S VISUALIZE OUR tract usage in a histogram\n",
    "n_bins=50\n",
    "avgTractUse = 0.\n",
    "statePop = np.sum(tractPop)\n",
    "for t in range(nTracts):\n",
    "    avgTractUse += tractUse[t] * tractPop[t]/statePop\n",
    "sumVarTractUse = 0.\n",
    "for t in range(nTracts):\n",
    "    sumVarTractUse += (tractUse[t]-avgTractUse)**2 *tractPop[t]/statePop\n",
    "sdTractUse = sumVarTractUse ** 0.5\n",
    "\n",
    "print(sdTractUse,\"=\",STATE,\"std dev of TRACT usage.  Here is its weighted histogram\")\n",
    "fig, ax = plt.subplots(tight_layout=True)\n",
    "# We can set the number of bins with the *bins* keyword argument.\n",
    "ax.hist(tractUse, bins=n_bins, weights=tractPop)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 77,
   "id": "121f9869-3d25-4594-93b3-64a57a95aca8",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "avg precinct use was  0.7023938128617214  over nonpanhandle precincts, adj state vtd Pop of 2080834.0\n",
      "0.26281139766494477 = std dev of PRECINCT usage.  Here is its weighted histogram\n",
      "this is a histogram of PRECINCT usage by precinct for IN\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# LET'S VISUALIZE OUR precinct usage in a histogram  #OOPS, OVERWROTE FLORIDA HERE\n",
    "#exclude the panhandle in stats\n",
    "onMap = [1.]*nPrecincts\n",
    "STATvtdPop = 0.  #this will be the vtdPop that \"counts\" for statistics\n",
    "for p in range(nPrecincts):\n",
    "    if vtdGeom[p].centroid.x < -86.149:  #is this a panhandle precinct?\n",
    "        onMap[p] = 0  #then don't count in stats\n",
    "    else:\n",
    "        STATvtdPop += vtdPop[t]\n",
    "n_bins=50\n",
    "avgPrecinctUse = 0.\n",
    "stateVTDPop = STATvtdPop  #np.sum(vtdPop)\n",
    "for p in range(nPrecincts): \n",
    "    avgPrecinctUse += precinctUse[p] * onMap[p]* vtdPop[p]/stateVTDPop\n",
    "sumVarPrecinctUse = 0.\n",
    "print(\"avg precinct use was \",avgPrecinctUse,\" over nonpanhandle precincts, adj state vtd Pop of\",stateVTDPop)\n",
    "for p in range(nPrecincts):\n",
    "    sumVarPrecinctUse += onMap[p]*(precinctUse[p]-avgPrecinctUse)**2 *vtdPop[p]/stateVTDPop\n",
    "sdPrecinctUse = sumVarPrecinctUse ** 0.5\n",
    "\n",
    "print(sdPrecinctUse,\"= std dev of PRECINCT usage.  Here is its weighted histogram\")\n",
    "print(\"this is a histogram of PRECINCT usage by precinct for\",STATE)        \n",
    "fig, ax = plt.subplots(tight_layout=True)\n",
    "# We can set the number of bins with the *bins* keyword argument.\n",
    "ax.hist(precinctUse, bins=n_bins, weights=vtdPop)\n",
    "plt.show()\n",
    "# IGNORE BELOW -- INCORRECT FLORIDA OVERWRITE"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 79,
   "id": "f6f03dcb-c722-4789-b47f-fb7aa3be97bc",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "here's a look at numerical stability - number of loops, avg =  7.4954\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "avgTLC = 0.\n",
    "usedTracts = 0.\n",
    "for t in range (nTracts):\n",
    "    if(tractLoopCounter[t] > 0):\n",
    "        avgTLC += tractLoopCounter[t]\n",
    "        usedTracts +=1.\n",
    "avgTLC = round(avgTLC / usedTracts, 4)\n",
    "print(\"here's a look at numerical stability - number of loops, avg = \",avgTLC)\n",
    "n_bins=20     \n",
    "fig, ax = plt.subplots(tight_layout=True)\n",
    "# We can set the number of bins with the *bins* keyword argument.\n",
    "ax.hist(tractLoopCounter, bins=n_bins)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "id": "974631b2-10ef-4847-af2e-a1e26b6fa768",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "this is a histogram of home-district pct GOP by tract\n",
      "statewide vote is off by 0.00824 0.59022 HD avg vs true 0.58198\n"
     ]
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# LET'S VISUALIZE OUR HOME DISTRICT red-blue lean in a histogram  and check the statewide vote vs. HD average\n",
    "n_bins=50\n",
    "print(\"this is a histogram of home-district pct GOP by tract\") \n",
    "print(\"statewide vote is off by\",round((stateGOP2-stateGOP),5),round(stateGOP2,5),\"HD avg vs true\",round(stateGOP,5))\n",
    "fig, ax = plt.subplots(tight_layout=True)\n",
    "# We can set the number of bins with the *bins* keyword argument.\n",
    "ax.hist(HDvGOP, bins=n_bins,weights=HDweight)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 86,
   "id": "720d9eb5-11e8-45f7-b402-a0048e9ddb99",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "calculated statewide Hispanic pct was 0.06302, should have been 0.06802\n",
      "this is a histogram of home-district VAP pct Hispanic by tract\n"
     ]
    },
    {
     "data": {
      "image/png": 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lSTrLig5jVgEnBpZngd/sMGZVx30BSLKd3tUXwP8lOdZhbleia4Afjupg+fSojtS0kZ6zZcJztnies8Ub1Tn75WEruwQqQ9ZVxzFd9u2trNoL7O0wnytakumqmrrc87iSeM4Wz3O2eJ6zxVvqc9YlULPAmoHl1cDJjmNWdthXkqSzdPkd1GFgfZJ1SVYC24AD88YcAG7uf5tvE/B8VZ3quK8kSWdZ8Aqqqs4k2QkcAiaAfVV1JMmO/vY9wEHgBmAGeBG45Xz7Lsk7uXKM/ceYS8Bztnies8XznC3ekp6zVA39lZAkSZeVd5KQJDXJQEmSmmSglkiH20P9cf+2UE8k+U6St16Oebak622xkvxGkp8m+cClnF+LupyzJO9K8niSI0n++VLPsTUd/t/8uST/kOS7/XN2y+WYZyuS7EvybJLvnWP7OW91d9GqyseIH/S+EPIfwBvpfdX+u8CGeWPeDryu/3wL8PDlnnfr52xg3DfofTHnA5d73q2fM+Bq4CngDf3lX7jc874CztkngU/3n08CzwErL/fcL+M5eydwPfC9c2y/AfhHen/vddMof5Z5BbU0FrzFU1V9p6r+p7/4EL2/I7acdb0t1oeBLwPPXsrJNarLOfsj4P6q+gFAVS3389blnBXw2iQBXkMvUGcu7TTbUVXfoncOzuVct7q7aAZqaZzr1k/n8if0/gSynC14zpKsAv4Q2HMJ59WyLv+d/SrwuiQPJHkkyc2XbHZt6nLO/gp4M72bCjwJ3F5VL1+a6V2RFvvzrrMud5LQ4nW+xVOS36UXqN9e0hm1r8s5+xzw8ar6ae8Pt8tel3O2Avh14D3AK4EHkzxUVf++1JNrVJdz9gfA48C7gV8Bvp7kX6rqhSWe25Wq88+7xTJQS6PL7aFI8hbgHmBLVf33JZpbq7qcsylgfz9O1wA3JDlTVX93SWbYnq63IfthVf0Y+HGSbwFvBZZroLqcs1uAO6v3C5aZJN8HrgP+9dJM8YrT6efdhfAjvqWx4C2ekrwBuB/40DL+0+ygBc9ZVa2rqrVVtRa4D/jTZRwn6HYrsb8HfifJiiSvovevCRy9xPNsSZdz9gN6V5wk+UXgTcDxSzrLK8u5bnV30byCWgLV7fZQfw78PPDX/SuCM7WM76Tc8ZxpQJdzVlVHk3wNeAJ4GbinqoZ+XXg56Pjf2V8Af5vkSXofX328qpbtP8OR5EvAu4BrkswCnwJeAee/1d1IXrv/NUFJkpriR3ySpCYZKElSkwyUJKlJBkqS1CQDJUlqkoGSJDXJQEmSmvT/3YW9edK4p9QAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# LET'S VISUALIZE OUR home district pct Hispanic in a histogram\n",
    "print(\"calculated statewide Hispanic pct was {0}, should have been {1}\"\n",
    "      .format(round(stateHisp2,5), round(np.sum(tractHisp)/np.sum(tractVAP),5) ) )\n",
    "n_bins=50\n",
    "print(\"this is a histogram of home-district VAP pct Hispanic by tract\")        \n",
    "fig, ax = plt.subplots(tight_layout=True)\n",
    "# We can set the number of bins with the *bins* keyword argument.\n",
    "ax.hist(HDvHisp, bins=[0.1,0.15,0.2,0.25,0.3,0.35,0.4,0.45,0.5,0.55,0.6,0.65,0.7,0.75,0.8,0.85,0.9,0.95,1.0],\n",
    "        weights=HDweight)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 85,
   "id": "1ab7704f-a226-45a9-98a6-d0684522c1eb",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "this is a bar plot of seats by VAP pct Hispanic for PA\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# ALTERNATE VIEW OF HISPANIC VOTE: HOW MANY DISTRICTS PER 10pct BIN of PCT HISPANIC VOTE?\n",
    "n_bins = 20\n",
    "HispSeats = [0.]*n_bins\n",
    "binMid = [0.]*n_bins\n",
    "for b in range(n_bins):\n",
    "    binMid[b]= float(b)/n_bins + 0.5/n_bins  #centering each bin\n",
    "for t in range(nTracts) :\n",
    "    b = int(HDvHisp[t]*n_bins)\n",
    "    HispSeats[b] += HDweight[t]*nDistricts  #multiply by nDistricts to get number of expected seats\n",
    "\n",
    "print(\"this is a bar plot of seats by VAP pct Hispanic for\",STATE)        \n",
    "fig, ax = plt.subplots()\n",
    "plt.bar(binMid,HispSeats,width=0.09 )\n",
    "#plt.scatter(binMid,HispSeats )\n",
    "ax.set(xlabel=\"pct Hispanic\", ylabel=\"number of seats\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 87,
   "id": "3ef49161-368a-40a2-83ff-92187edb3462",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "using a threshold of 0.35 the number of Hisp districts should be 0.0\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Where in the state do we see Hispanic-heavy home districts?\n",
    "minHisp1 = 0.35\n",
    "minHisp2 = 0.40\n",
    "sumHispDistricts = 0.\n",
    "for t in range(nTracts):\n",
    "    if (HDvHisp[t] > minHisp1 and tractPop[t] > minTractPop):\n",
    "        sumHispDistricts += tractPop[t]/avgDistrictPop\n",
    "        if (HDvHisp[t] < minHisp2):\n",
    "            plt.scatter(tractCPx[t],tractCPy[t],marker='.',color='orange' )\n",
    "        else :            \n",
    "            plt.text(tractCPx[t],tractCPy[t],'o',color='blue', fontsize= 12)\n",
    "print(\"using a threshold of\",minHisp1,\"the number of Hisp districts should be\",round(sumHispDistricts,3))\n",
    "x,y = origMAP.exterior.xy\n",
    "plt.plot(x,y,c=\"green\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "id": "a92133ad-dc95-4aa1-af02-c1d71296cee6",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "this is a bar plot of seats by VAP pct Black for IN\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# ALTERNATE VIEW OF BLACK VOTE: HOW MANY DISTRICTS PER 10pct BIN of PCT BLACK VOTE?\n",
    "n_bins = 10\n",
    "BlackSeats = [0.]*n_bins\n",
    "binMid = [0.]*n_bins\n",
    "for b in range(n_bins):\n",
    "    binMid[b]= float(b)/n_bins + 0.5/n_bins  #centering each bin\n",
    "for t in range(nTracts) :\n",
    "    b = int(HDvBlack[t]*n_bins)\n",
    "    BlackSeats[b] += HDweight[t]*nDistricts  #multiply by nDistricts to get number of expected seats\n",
    "\n",
    "print(\"this is a bar plot of seats by VAP pct Black for\",STATE)        \n",
    "fig, ax = plt.subplots()\n",
    "plt.bar(binMid,BlackSeats,width=0.09 )\n",
    "ax.set(xlabel=\"pct Black\", ylabel=\"number of seats\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "id": "b1e9c437-ddf7-4202-aff0-19f338a5d013",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Black candidates would expect to win 4.701 GA seats out of 14\n",
      "Using simple Alabama-style of Blacks voting 0.9 Dem and whites voting 0.8 GOP\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Where in the state do we see opportunity home districts?  #NEW FORMULATION based on ALABAMA\n",
    "isOppor = [0]*nTracts #will track whether this is an opportunity district\n",
    "blackFracSeats = 0.\n",
    "blackPctDem = 0.9  #fraction of Black voters who are Democrats (est'd from Chen / Stephanopoulos)\n",
    "whitePctGOP = 0.8   #fraction of White voters who are Republican  (\"  \")\n",
    "#minBlack1 = 0.30\n",
    "#minBlack2 = 0.50\n",
    "for t in range(nTracts):\n",
    "    pctWhiteDem = (1-whitePctGOP)*(1-HDvBlack[t])  #this is the fraction of voters who are white Democrats\n",
    "    pctBlackDem = blackPctDem * HDvBlack[t]  #fraction of voters who are black Democrats\n",
    "    if tractPop[t] > minTractPop and HDvGOP[t] < 0.5 :  #Dems would win this Home District\n",
    "        if pctBlackDem > pctWhiteDem : #Blacks would win the primary\n",
    "            isOppor[t] = 1\n",
    "            plt.scatter(tractCPx[t],tractCPy[t],marker='.',color='blue' )\n",
    "            blackFracSeats += HDweight[t]\n",
    "        else :  #the White Dem candidate would win the election          \n",
    "            plt.scatter(tractCPx[t],tractCPy[t],marker='.',color='gray' )\n",
    "\n",
    "print(\"Black candidates would expect to win\",round(blackFracSeats*nDistricts,3),STATE,\"seats out of\",nDistricts)\n",
    "print(\"Using simple Alabama-style of Blacks voting\",blackPctDem,\"Dem and whites voting\",whitePctGOP,\"GOP\")\n",
    "x,y = wholeMAP.exterior.xy\n",
    "plt.plot(x,y,c=\"green\")\n",
    "plt.show()\n",
    "        "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "id": "c5125128-e4bb-4337-bc5e-3f48d7cf404f",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Here's the GA map with Hisp+Black greater than  0.4 or even 0.5\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Where in the state do we see Black+Hispanic heavy home districts?\n",
    "minMinor = 0.40 \n",
    "minMinor2 = 0.50\n",
    "print(\"Here's the\",STATE,\"map with Hisp+Black greater than \",minMinor,\"or even\",minMinor2)\n",
    "for t in range(nTracts):\n",
    "    if ((HDvBlack[t] + HDvHisp[t]) > minMinor and tractPop[t] > minTractPop):\n",
    "        if (HDvBlack[t] + HDvHisp[t]) > minMinor2 :\n",
    "            plt.scatter(tractCPx[t],tractCPy[t],marker='.',color='blue' )\n",
    "        else :            \n",
    "            plt.scatter(tractCPx[t],tractCPy[t],marker='.',color='gray' )\n",
    "\n",
    "x,y = tractMAP.exterior.xy\n",
    "plt.plot(x,y,c=\"green\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "id": "58119ef8-13ef-4135-9843-6518025dda07",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "this is a histogram of home-district population by tract; avg =  765136.2142857143\n"
     ]
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# LET'S VISUALIZE OUR HOME DISTRICT population in a histogram\n",
    "n_bins=50\n",
    "print(\"this is a histogram of home-district population by tract; avg = \",np.sum(tractPop)/nDistricts)        \n",
    "fig, ax = plt.subplots(tight_layout=True)\n",
    "# We can set the number of bins with the *bins* keyword argument.\n",
    "a = avgDistrictPop\n",
    "ax.hist(HDvPop, bins=[0.9*a,0.92*a,0.95*a,0.99*a,1.0*a,1.01*a,1.05*a,1.1*a])   #n_bins\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "id": "1f9101bd-108a-48cb-bee5-3f4e04c18132",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "this is a histogram of home-district area by tract\n"
     ]
    },
    {
     "data": {
      "image/png": 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Ms6q6VlUPsXSGmu1JPj3ikcZWks8Bl6vq1KhnWc4kBMrTKqlpSe5kKU7fraofjHqeSVFVvwNewfdL1+JR4PNJ3mbp7ZHHknxntCP9v0kIlKdVUrOSBPgWcLaqvj7qecZdkpkkn+y2Pw58BnhjpEONsao6UFWbq2qWpe+dP6qqL454rD8Z+0BV1VXgg9MqnQWO3qbTKk2sJN8DfgL8dZKFJHtGPdMYexT4Eks/mb7e/frsqIcaYxuBl5P8gqUfTo9XVVMfjdbgeKojSVKTxv4ISpI0mQyUJKlJBkqS1CQDJUlqkoGSJDXJQEmSmmSgJElN+j8Fv5bOTEXqagAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# more HOME DISTRICT STATS in a histogram\n",
    "n_bins=50\n",
    "print(\"this is a histogram of home-district area by tract\")        \n",
    "fig, ax = plt.subplots(tight_layout=True)\n",
    "# We can set the number of bins with the *bins* keyword argument.\n",
    "ax.hist(HDarea, bins=n_bins)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "id": "d8167a80-2b91-4adc-ab34-9e010838213d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# What is the correlation of HD area to red-blue lean?\n",
    "fig, ax = plt.subplots()\n",
    "plt.scatter(HDarea,HDvGOP,marker='.' )\n",
    "ax.set(xlabel=\"tract area\", ylabel=\"home district pct GOP\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "id": "cf95cc7c-e58a-4174-94f6-b6d4c7ccb0ff",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# What is the correlation of tract usage to red-blue lean?\n",
    "fig, ax = plt.subplots()\n",
    "plt.scatter(HDvGOP,tractUse, marker='.' )\n",
    "ax.set(xlabel=\"home district pct GOP\", ylabel=\"tractUse\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "id": "617384b8-1e0b-45a7-ae09-babe8dd9d429",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# What is the correlation of tract usage to tract population?\n",
    "fig, ax = plt.subplots()\n",
    "plt.scatter(tractPop,tractUse, marker='.' )\n",
    "ax.set(xlabel=\"Census tract population\", ylabel=\"tractUse\")\n",
    "ax.set_xlim(left=0,right=10000)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "id": "91f163ab-6b94-47ee-bd29-be3e751fe2b4",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# UPDATED VOTES-SEATS CURVE (from votes2seats.ipynb 1/15/22)\n",
    "# LET'S REWORK OUR VOTES - SEATS CALCULATIONS\n",
    "# FIRST, LET'S DO THE BASIC VOTES-TO-SEATS, no fractional seats\n",
    "# INPUTS ARE HDvGOP[nTracts] and HDweight[nTracts]\n",
    "# HDvGOP is the redness lean of each Census tract's Home District\n",
    "# HDweight is the population of this Census tract relative to the state population\n",
    "# stateGOP is the statewide fraction of GOP vote vs. GOP + Dem vote\n",
    "# KEY EQUATION:\n",
    "# expected Seats at a given vote V = sum(HDweight) for tracts with HDvGOP > 0.5 + (V - stateGOP)\n",
    "# for easy calculation, create ~1000 bins, each bin containing HD's in a dV vote window\n",
    "nBins = 1000\n",
    "dV = 1./nBins\n",
    "binWeight = [0.]*nBins\n",
    "\n",
    "for t in range(nTracts):\n",
    "    binNo = int(HDvGOP[t]*nBins)   #which vote bin does this tract's vote go into for the expected statewide vote?\n",
    "    binWeight[binNo] += HDweight[t]\n",
    "    \n",
    "binVote = [0.]*nBins  #this will store the statewide vote for this bin\n",
    "for b in range(nBins) :\n",
    "    binVote[b] = b*dV\n",
    "\n",
    "fig, ax = plt.subplots()\n",
    "plt.plot(binVote, binWeight, marker='.',linestyle=\"none\")\n",
    "ax.set(xlabel=\"district pct GOP\", ylabel=\"bin weight\")\n",
    "plt.show()   #this should resemble above histogram"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "id": "85608d3f-c3bd-4d91-9378-16270ceeb9b6",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#continuing with votes-to-seats, let's compute the S(V) curve with no fractional-seat smearing\n",
    "cumVote = [0.]*nBins\n",
    "for b in range(nBins) :\n",
    "    cumVote[nBins-b-1] = np.sum(binWeight[:(b+1)])  #cumulative weight of all Home Districts below vote b*dV\n",
    "    # print(\"bin, bin vote, cumVote for this bin\",b,b*dV,cumVote[b] )\n",
    "    \n",
    "cumSeats = [0.]*nBins  #this will sum the seats earned for a given statewide vote\n",
    "stateVoteBin = int( (stateGOP+0.5*dV)*nBins )   #which bin (out of 1/dV) holds the statewide total vote?\n",
    "for b in range(nBins) :\n",
    "    bb = int( max(0,min(nBins-1,nBins/2 + b-stateVoteBin)) )\n",
    "    cumSeats[b] = 1. - cumVote[bb]\n",
    "\n",
    "fig, ax = plt.subplots()\n",
    "plt.plot(binVote, cumSeats, marker='.',linestyle=\"none\")\n",
    "ax.set(xlabel=\"statewide vote, nBins =\"+str(nBins)+\",\"+str(STATE), ylabel=\"GOP seats won (no fractl smearing)\")\n",
    "RANGE = [0.1, 0.9]\n",
    "fifty50 = [0.5, 0.5]\n",
    "expected = [stateGOP, stateGOP]\n",
    "plt.plot(RANGE,fifty50)\n",
    "plt.plot(fifty50,RANGE)\n",
    "plt.plot(expected,RANGE, linestyle=\"--\",color='red')\n",
    "\n",
    "#LET'S ALSO crudely ESTIMATE RESPONSIVENESS near the STATEWIDE VOTE, with a pseudonormal weighting and lst-sq fit\n",
    "stateSigma = 0.03  #User-adjustable, this is the uncertainty in the statewide vote from election to election\n",
    "usedBins = int(stateSigma/dV)\n",
    "nFitPoints = 6*usedBins\n",
    "voteData = [0.]*nFitPoints\n",
    "seatData = [0.]*nFitPoints\n",
    "counter = 0\n",
    "for b in range(nBins):\n",
    "    if ( abs(b-stateVoteBin) <= 2*usedBins ):  #include this S-V pair in our line fit\n",
    "        voteData[counter]=binVote[b]\n",
    "        seatData[counter]=cumSeats[b]\n",
    "        counter += 1\n",
    "        # print(b,counter)\n",
    "        if ( abs(b-stateVoteBin) < usedBins) : #double count this in data set\n",
    "            voteData[counter]=binVote[b]\n",
    "            seatData[counter]=cumSeats[b]\n",
    "            counter +=1\n",
    "fit = np.polyfit(voteData,seatData,1)  #first-order linear regression with old polyfit\n",
    "Rsimple = fit[0]     #slope is fit[0] in y = mx + b, intercept is fit[1]\n",
    "y0 = fit[1]\n",
    "Rx = [stateGOP - 4.*stateSigma, stateGOP + 4.*stateSigma]\n",
    "Ry = [y0 + Rx[0]*Rsimple, y0 + Rx[1]*Rsimple]\n",
    "plt.plot(Rx,Ry ) \n",
    "ax.text(Rx[1]+0.02, Ry[1]+0.02, \"R = \"+str(round(Rsimple,4)), transform=ax.transAxes, fontsize=14,color='purple')\n",
    "expectedSeats = cumSeats[stateVoteBin]\n",
    "ax.text(0.02,expectedSeats+0.03,\"expected seats = \"+str(round(expectedSeats,3)),transform=ax.transAxes,fontsize=9)\n",
    "ax.text(stateGOP+0.01,0.1,\"HD statewide vote = \"+str(round(stateGOP2,3)),transform=ax.transAxes,fontsize=9)\n",
    "Ex = [0,stateGOP ]\n",
    "Ey = [expectedSeats, expectedSeats]\n",
    "plt.plot(Ex,Ey, linestyle='-.',color='red')\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "id": "ce47266f-e2ea-4649-b10c-e8225994c122",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#3/6/22 - alternate votes-to-seats with fractional seat smearing.\n",
    "# First, compute the general cdf sliver for -3 sigma to +3 sigma\n",
    "# Then, apply this smearing to the binVote weights\n",
    "seatVar = 0.04\n",
    "nBins = 1000\n",
    "nSigma = 6.  #how many total sigma of deviation we are explicitly modeling; the rest go into the tails\n",
    "nSlivers = 2*int(3*nBins*seatVar)  #budget for 3 sigma on either side of the expected vote\n",
    "sliverWt = [0.]*nSlivers  #each sliver holds the cdf reflecting the relative distance from the mean vote\n",
    "sliverSigma = [0.]*nSlivers\n",
    "totalSliverWt = 0.\n",
    "for nS in range(nSlivers):\n",
    "    dSigmaHigh = nSigma* (nS+0.5 - nSlivers/2) / float(nSlivers)\n",
    "    dSigmaLow =  nSigma* (nS-0.5 - nSlivers/2) / float(nSlivers)\n",
    "    sliverSigma[nS] = 0.5*(dSigmaHigh+dSigmaLow)  #for plotting; keeps track of this sliver's center sigma\n",
    "    sliverWt[nS] = norm.cdf(dSigmaHigh) - norm.cdf(dSigmaLow)\n",
    "    totalSliverWt += sliverWt[nS]\n",
    "lowSliverWt = 0.5 * (1. - totalSliverWt)\n",
    "highSliverWt = lowSliverWt\n",
    "#print(\"each tail of distribution has weight\",round(lowSliverWt,4) )\n",
    "#plt.plot(sliverSigma,sliverWt)\n",
    "#plt.show()\n",
    "# OK, that worked as expected.  Now, do the smearing of each bin\n",
    "smearedBinWeight = [0.]*nBins\n",
    "for b in range(nBins):\n",
    "    centerBinNo = b #int(HDvGOP[t]*nBins)   #which vote bin does this tract's vote go into for the expected statewide vote?\n",
    "    binOffset = int(nSlivers/2)\n",
    "    for nS in range(nSlivers):\n",
    "        binNo = centerBinNo + nS - binOffset\n",
    "        if (binNo < 0): #deep blue district; variance would push vote %R < 0\n",
    "            binNo = 0\n",
    "        if (binNo >= nBins):  #deep red district; variance would push vote %R > 1\n",
    "            binNo = nBins - 1\n",
    "        smearedBinWeight[binNo] += binWeight[b]*sliverWt[nS]\n",
    "    # Now put the tails into the correct bins\n",
    "    binNo = centerBinNo -1 - binOffset  #left tail\n",
    "    if binNo < 0:\n",
    "        binNo = 0\n",
    "    smearedBinWeight[binNo] += binWeight[b]*lowSliverWt\n",
    "    binNo = centerBinNo + nSlivers - binOffset  #right tail\n",
    "    if binNo >= nBins:\n",
    "        binNo = nBins -1 \n",
    "    smearedBinWeight[binNo] += binWeight[b]*highSliverWt\n",
    "\n",
    "fig, ax = plt.subplots()\n",
    "plt.plot(binVote, binWeight, marker='.',linestyle=\"none\",label=\"unsmeared\")\n",
    "plt.plot(binVote, smearedBinWeight, marker='o',linestyle=\"none\",label=\"smeared\")\n",
    "RANGE = [0.0, 0.5*np.max(binWeight)]\n",
    "sGOP = [stateGOP, stateGOP]\n",
    "plt.plot(sGOP,RANGE,linestyle=\"-\",label=\"statewide \"+str(round(stateGOP,3)) )\n",
    "ax.set(xlabel=\"district pct GOP\", ylabel=\"bin weight\")\n",
    "plt.legend()\n",
    "plt.show()   #this should resemble above histogram"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "id": "8a2d7de3-ea93-4447-bb69-36defbae38df",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#continuing with votes-to-seats, let's compute the S(V) curve with no fractional-seat smearing\n",
    "smearedBinVote = [0.]*nBins  #this will store the statewide vote for this bin\n",
    "cumSmearedVote = [0.]*nBins\n",
    "for b in range(nBins) :\n",
    "    smearedBinVote[b] = b*dV\n",
    "    cumSmearedVote[nBins-b-1] = np.sum(smearedBinWeight[:(b+1)])  #cumulative weight of all Home Districts below vote b*dV\n",
    "    # print(\"bin, bin vote, cumVote for this bin\",b,b*dV,cumVote[b] )\n",
    "    \n",
    "cumSmearedSeats = [0.]*nBins  #this will sum the seats earned for a given statewide vote\n",
    "stateVoteBin = int( (stateGOP+0.5*dV)*nBins )   #which bin (out of 1/dV) holds the statewide total vote?\n",
    "for b in range(nBins) :\n",
    "    bb = int( max(0,min(nBins-1,nBins/2 + b-stateVoteBin)) )\n",
    "    cumSmearedSeats[b] = 1. - cumSmearedVote[bb]\n",
    "\n",
    "fig, ax = plt.subplots()\n",
    "plt.plot(smearedBinVote,cumSmearedSeats, marker='o',linestyle=\"none\",label=str(seatVar)+\" smear\")\n",
    "plt.plot(binVote, cumSeats, marker='.',linestyle=\"none\",label=\"no smear\")\n",
    "ax.set(xlabel=\"true \"+STATE+\"-wide vote=\"+str(round(stateGOP,4))+\", nBins =\"+str(nBins), ylabel=\"GOP seats won\")\n",
    "RANGE = [0.1, 0.9]\n",
    "fifty50 = [0.5, 0.5]\n",
    "expected = [stateGOP, stateGOP]\n",
    "expectedSeats = cumSeats[stateVoteBin]\n",
    "expectedS = [expectedSeats,expectedSeats]\n",
    "smearedSeats = cumSmearedSeats[stateVoteBin]\n",
    "smearedS = [smearedSeats,smearedSeats]\n",
    "plt.plot(RANGE,fifty50)\n",
    "plt.plot(fifty50,RANGE)\n",
    "plt.plot(RANGE,smearedS, linestyle=\"--\",color='blue',label=str(round(expectedSeats,3))+\"no smear\")\n",
    "plt.plot(RANGE,expectedS, linestyle=\"--\",color='orange',label=str(round(smearedSeats,3))+\"smeared\")\n",
    "plt.plot(expected,RANGE, linestyle=\"--\",color='red')\n",
    "plt.legend()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "id": "597b7758-4f83-40f7-8909-4946e64267ad",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "simpler and fractional-seat-smeared (var of 0.04 ) responsiveness are 1.998 1.938\n",
      "fractional expected GOP seats =  7.286  out of  9 seats for IN\n",
      "simpler expected GOP seats =  7.3611  out of  9 seats\n"
     ]
    }
   ],
   "source": [
    "#Finally, let's compare responsiveness using smeared (fractional seat variance) to non-smeared calculated above\n",
    "#LET'S ALSO crudely ESTIMATE (smeared) RESPONSIVENESS near the STATEWIDE VOTE, as we did for non-smeared\n",
    "stateSigma = 0.03  #User-adjustable, this is the uncertainty in the statewide vote from election to election\n",
    "usedBins = int(stateSigma/dV)\n",
    "nFitPoints = 6*usedBins\n",
    "voteData = [0.]*nFitPoints\n",
    "seatData = [0.]*nFitPoints\n",
    "counter = 0\n",
    "for b in range(nBins):\n",
    "    if ( abs(b-stateVoteBin) <= 2*usedBins ):  #include this S-V pair in our line fit\n",
    "        voteData[counter]=smearedBinVote[b]\n",
    "        seatData[counter]=cumSmearedSeats[b]\n",
    "        counter += 1\n",
    "        # print(b,counter)\n",
    "        if ( abs(b-stateVoteBin) < usedBins) : #double count this in data set\n",
    "            voteData[counter]=smearedBinVote[b]\n",
    "            seatData[counter]=cumSmearedSeats[b]\n",
    "            counter +=1\n",
    "fit = np.polyfit(voteData,seatData,1)  #first-order linear regression with old polyfit\n",
    "Rsmeared = fit[0]     #slope is fit[0] in y = mx + b, intercept is fit[1]\n",
    "y0 = fit[1]\n",
    "\n",
    "print(\"simpler and fractional-seat-smeared (var of\",seatVar,\") responsiveness are\",round(Rsimple,3) ,round(Rsmeared,3) )   \n",
    "print(\"fractional expected GOP seats = \",round(nDistricts*smearedSeats,3),\" out of \",nDistricts,\"seats for\",STATE)\n",
    "print(\"simpler expected GOP seats = \",round(nDistricts*expectedSeats,4),\" out of \",nDistricts,\"seats\")\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "86f82727-7193-4194-aa61-2b99408dbcb6",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
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   "language": "python",
   "name": "python3"
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    "name": "ipython",
    "version": 3
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   "pygments_lexer": "ipython3",
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